Guest Post by Willis Eschenbach
I got to wandering through the three main datasets that make up the overall CERES data, and I noticed an odd thing. The three main datasets are the all-sky downwelling solar, upwelling reflected solar, and upwelling longwave radiation, measured in watts per square metre (W/m2). Here are those three datasets:
Figure 1 the three main datasets that make up the CERES all-sky data. Note that as you’d expect, total input (solar ~340 W/m2) equals total output (100 W/m2 reflected plus 240 W/m2 radiation).
What I’d never noticed before is that the three datasets are all running on different clocks. One peaks in December, one peaks in January, and one peaks in July. Not only that, they all have different cycles of rising and falling … go figure.
A word of foreshadowing. I have no particular point to make in this post. Instead, it is a meander, an appreciative inquiry into the components of the shortwave (solar) and longwave (thermal infrared) top-of-atmosphere radiation. And at the end of the day, I suspect you’ll find it contains more questions and wonderment and curiosities than it has answers and insights. So hop on board, the boat’s leaving the dock, there’s a forecast of increasing uncertainty with a chance of scattered befuddlement … what’s not to like?
First, the solar input. Although a lot of folks talk about the “solar constant”, over the course of the year the sun is anything but constant. Because the Earth’s orbit is not circular, annually the Earth moves closer and further from the sun. This gives an annual change of about 22 W/m2, with a high point in early January and a low point exactly six months later in early July. So that’s one clock—peaks in January, bottoms out in July, six months rise, six months fall.
Figure 2. Downwelling solar. Top panel shows actual data. Middle panel shows the regular seasonal variation. The bottom panel shows the residual, calculated as the data minus the seasonal component. Horizontal gold dashed lines show ± one standard deviation of the residual data. This range encompasses about 2/3 of the data. Vertical dashed and dotted lines show January (dashed) and July (dotted).
The sun, of course, is very stable, so the actual variation looks just like the seasonal variation. Note that the standard deviation of the residuals is only about plus or minus a tenth of a watt, which is a variation of about 0.03%, three hundredths of one percent of the size of the signal. In passing, the cyclical variation of about ± 0.03% you see highlighted by the blue line in the bottom panel is the TSI (total solar irradiation) variation associated with the sunspot cycle … but I digress, if one can do that while aimlessly meandering …
The next dataset, reflected solar, is on a slightly different clock. While reflected solar naturally varies with the strength of the sun, it actually peaks in December rather than January.
Figure 3. Reflected (upwelling) solar. Top panel shows actual data. Middle panel shows the regular seasonal variation. The bottom panel shows the residual, calculated as the data minus the seasonal component. Horizontal gold dashed lines show ± one standard deviation of the residual data. This range encompasses about 2/3 of the data. Vertical dashed and dotted lines show January (dashed) and July (dotted).
To me, this is a very curious signal. To start with, it is at a minimum in August, and a maximum in December. So it rises quickly for four months, then falls for eight months, and repeats. Odd.
In addition, it’s curious because it is so stable. Of the three datasets (downwelling solar, reflected solar, and longwave), the reflected solar is the only one that is unconstrained. The downwelling solar is basically fixed. And the upwelling longwave is physically constrained—in the long run (although not the short run) what goes out is limited by what goes in.
But the variations in reflected solar, both geographical and temporal, are not fixed. Given the varying annual snow, ice, and cloud cover in the polar regions, plus the varying tropical cloud cover, plus the differences in clouds over the extra-tropical areas, there’s nothing obvious that constrains reflected sunlight to be the same, year after year … and yet, as Figure 3 shows, the standard deviation of the residuals is only half a watt per square metre, that’s plus or minus half a percent. And that means that 95% of the months are within one watt of the seasonal average to me. To me, that’s a wonder.
Finally, here is the longwave. Upwelling longwave is basically a function of temperature, so it peaks in the northern hemisphere summer. Of the three datasets, longwave varies the least over the course of the year.
Figure 4. Upwelling longwave radiation. Top panel shows actual data. Middle panel shows the regular seasonal variation. The bottom panel shows the residual, calculated as the data minus the seasonal component. Horizontal gold dashed lines show ± one standard deviation of the residual data. This range encompasses about 2/3 of the data. Vertical dashed and dotted lines show January (dashed) and July (dotted).
Again, we see only a small variation in the residuals, only ± half a watt per square metre, or about ± 0.2%, two tenths of a percent of the size of the signal. And again the signal is not symmetrical, with the peak in July and the minimum five months later in December. So globally, longwave rises for seven months, then drops for five months.
Having looked at that, I got curious about the strange shape of the seasonal variations in the reflected solar. So I decided to take a look at the latitudinal variations in the solar, reflected solar, longwave, and albedo.
Figure 5. Top of atmosphere (TOA) radiation by latitude. Area weighted. Note the units are terawatts (10^12 watts) per degree of latitude. Area-weighting is done using the official CERES latitude areas, which are for an oblate spheroid rather than a sphere. It makes no visible or numerical difference at this scale, but Gavin Schmidt busted me for not using it, and he’s right, so why not use the recommended data? The radiation in W/m2 is averaged for each degree of latitude. That average value is multiplied by the surface area of the degree of latitude (in square metres / ° latitude). The square metres cancel out, and we are left with watts per degree of latitude.
You can see the increased reflection from 0-10°N of the Equator. This is the sunlight reflecting from the massed cumulonimbus of the Inter-Tropical Convergence Zone (ITCZ). These tropical thunderstorms of the ITCZ provide the power driving the global equator-to-pole circulation of the atmosphere and the ocean. The increased reflection from 0-10°N is important because of the strength of the incoming sunshine. Half of the incoming TOA solar energy strikes the planet between 25°N and 25°S.
It’s also clear that the albedo in the southern polar regions is much higher than that of the northern polar regions. To investigate the effects of that difference on the radiation datasets, I decided to re-do Figure 5, the radiation by latitude, and look at the differences between June and December. Figure 6 shows June (darker of each pair of lines) and December (lighter lines) for the TOA solar, reflected, and longwave radiation.
Figure 6. As in Figure 5 (without albedo), but for June and December. For each pair of lines, the darker of the pair is the June data, and the lighter is the December data. The dotted blue line is the reverse (north/south) of the light blue line, and is shown in order to highlight the difference in reflected solar near the poles.
OK, so here we finally can see why the shape of the reflected solar data is so wonky. In December, there is much more solar reflection from the Antarctic region, with its very high albedo. December reflections at 70°S are about 500 TW/°. On the other hand, in June at 70°N the reflections are much smaller, only about 350 TW/°. As a result, when these regions swing into and out of view of the sun, we get large differences in reflected sunlight.
But the real surprise for me in Figure 6 was the upwelling longwave. The downwelling and reflected solar profiles are quite different from June to December … but to my shock, the upwelling longwave hardly changes at all. Say what? Heck, in the extra-tropical southern hemisphere there’s almost no difference at all in longwave radiation over the year … why so little change in either hemisphere?
And that, to me is the joy of science—not knowing which bush hides the rabbit … or the tiger.
Finally, Figure 7 shows the TOA net radiation imbalance. This is the downwelling solar energy, less what is reflected, less what is radiated.
Figure 7. Net top-of-atmosphere (TOA) radiation imbalance. Note that this is an anomaly, because there is a known error of about a 5 W/m2 difference in the incoming and outgoing CERES radiation data. So while we can use it for trends and standard deviations, it cannot tell us if there is an overall persistent imbalance in the TOA radiation. Positive values show the system gaining energy, and negative values show it losing energy. Panels as in previous figures, showing the data (top panel) along with the seasonal and residual components of the signal.
I see that this has the reverse of the four-month rise, eight-month fall pattern of the reflected data. The TOA imbalance falls for four months, and then rises for eight months.
Once again, however, the most surprising aspect of this net imbalance data is the amazing stability. There is no trend in the data, and the standard deviation of the residuals is only a bit above about half a watt per square metre.
Remember that this is a system that is moving huge, unimaginable amounts of energy, with average downwelling total surface radiation of half a kilowatt, and peak surface solar insolation of about a kilowatt. More importantly, it is a system with the significant albedo variables being nothing more solid than the ephemeral, seasonal, mutable phenomena of clouds, wind, snow, ice, and vegetation.
In such a system, it is something eminently worthy of study that over the thirteen years of the CERES dataset, for reflected solar and upwelling longwave, 95% of the months are within one watt/m2 of the seasonal average. Within one lousy watt! We assuredly do not know all the reasons why that might be so …
Anyhow, thanks for coming along. Looks like the weather forecast for the voyage was about right.
All the best to each of you,
w.
Standard Proclaimer: If you disagree with something that I or anyone has said, please QUOTE THE EXACT WORDS that you disagree with. Only then can we understand what it is you object to.
[UPDATE]:
DATA AND CODE: The code is in a zipped folder here. Unzip it and put the individual files into the workspace. You’ll also need the CERES TOA data in the same workspace (WARNNG: 230 Mbytes). The main file is called “Three Clocks.R”, I think it’s all turnkey.
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Data and code?
I knew that the clouds moderated the seasonal swings considerably, and I knew that the variation at TOA was less than at the surface, but I didn’t realise things were *that* stable. It almost looks from figure 6 as if the summer and winter TOA emission temperatures at temperate latitudes should be the same.
I’ve been trying to eyeball-integrate the difference between those summer-winter upwelling longwave graphs, convert back to W/m^2, and then turn that into a temperature difference. I got about 10 W/m^2 difference for the northern hemisphere gap, which corresponds to about 2 C difference in blackbody emission temperature. I could easily be a factor of 2 or 3 out, but am I an order of magnitude out?
Fascinating!
I think a large part of the stability of the reflected radiation is due to cloud feedback. (You certainly know about the equatorial cloud thermostat, so I’m no doubt teaching grandma to suck eggs, here.) Where you get cold air descending you get high pressure and clear skies, and the sunny weather warms things up. When warm air rises it’s usually laden with moisture picked up from the surface, forms clouds, and cuts off the sunlight, cooling things down. The feedback tends to push the temperature at the typical cloud-forming altitude towards the condensation point for water, and the temperature of the surface is therefore pushed towards a somewhat higher temperature because of the adiabatic lapse rate.
Of course, if temperature was all there was to it, we’d expect the warmer parts of the world to be permanently blanketed in cloud! But it’s also tied to the convective rise and fall of the air, so it’s actually a feedback on temperature *gradients*. In warmer areas the rising air has to go higher before it’s cold enough to condense, and the gap between cloud and surface has to be larger, giving a bigger adiabatic gap.
Maybe. I’m not sure. Clouds seem like a difficult problem.
Note the sharp drop around Aug/Sept of 2003. That coincides with a tiny La Nina on the Multi Enso, which happens to sit right in the middle of a long stretch of El Nino. I note that in 2011 the upwelling long wave moves sharply upward for 1 month then tapers off for the next 3 months. I had moved into the mountains in mid April that year. All the way through to the end of May of 2011, the temps where I was at did not rise above 50Fdaytime until close till the end of May. I was located in a cold spot, though. The next year 2012 was somewhat similar except the cold broke earlier and there was a more normal spring.
Look at the upwelling LW minus seasonal. That is a perfect match to the Multivariate ENSO Index. It shows the cooling in early 2000s, and then the long warming which follows, with the dip between 2008/09.
I take it that the upwelling mass in the ITCZ zone creates a bow in the atmosphere that splits the push on the atmosphere from centrifugal force of the spin of the Earth? That then forces the energy of the system north and south? I see that the albedo diagram explains why the southern pole is more important to what is now taking place at the two poles.
Willis: Very nice and quite surprising as you say.
You might like to have a look at how well Fig 4 corresponds to the measured temperature data sets as well. They appear to have quite a close relationship which one would expect as one rises so the other does. A validation of the result as presented.
http://climatedatablog.files.wordpress.com/2014/02/hadcrut-giss-rss-and-uah-global-annual-anomalies-aligned-1979-2013-with-gaussian-low-pass-and-savitzky-golay-15-year-filters-1979-on.png
Off subject, but what could one glean from examination of the global warming curve over the past decades for which plausible data exists and what one would expect it to be, given a variety of
climate sensitivity estimates? If atmospheric CO2 mass has a log relationship with temps, why
do folks believe the slope/shape of warming during the 1980-1998 time period graphs make any sense, especially when claimed that CO2 induced warming is the only temp change occurring?
Highly interesting post Willis, Thanks. Could a fourth clock be the “roughness”of the ocean surfaces. For example: More powerful trade winds as is the mantra of the day (humor) causing less sun radiation reflection.
Figure 7 might give some the false impression that high ENSO means the system is losing energy (negative imbalance around 2010) but that’s just because of the delay between the energy input and output.
http://virakkraft.com/Rad-TOA.png
Dare I say Willis’ work suggests Gaia? And wouldn’t it be weird/ wonderful if it turns out Gaia is the reason CO2 makes little difference? The “save our planet” types needn’t have worried. Our planet is pretty good at saving itself.
I was struck by the slight but distinct downward trend in the minimums of the top graph of Figure 4, the upwelling long-wave radiation at the top of the atmosphere, (it also seems to be present in the maximums, perhaps to a lesser extent). This long-wave radiation is heat radiation leaving the earth, so wouldn’t its value go up and down as the average temperature of the earth’s surface and all levels of the atmosphere increased and decreased? If in fact it is a reasonable proxy for the average temperature of the thin film of material — on a planetary scale — covering the planet’s rocky core, this graph suggests there is definite global cooling, not just a “pause”.
http://upload.wikimedia.org/wikipedia/commons/4/4e/Precession_and_seasons.jpg
Willis,
“But the real surprise for me in Figure 6 was the upwelling longwave. The downwelling and reflected solar profiles are quite different from July to December … but to my shock, the upwelling longwave hardly changes at all. Say what? Heck, in the extra-tropical southern hemisphere there’s almost no difference at all in longwave radiation over the year … why so little change in either hemisphere?”
To me, it is fairly obvious – the northern hemisphere is mainly land so there is a large surface temperature difference from winter to summer but in the southern hemisphere, it is mainly ocean so the surface temperature difference will be fairly small. However, even in the northern hemisphere, the Pacific & Atlantic ocean area would moderate the northern hemispheric signal. The small slices of South America, Africa & Australia are not enough to impact the total southern hemispheric LWIR signal.
Jeff
This 340W/m2 is not TOTAL irradiance but part of same. Total solar radiation is 1370W/m2. As measured in space and easily calculated. See one of Joseph Postma’s papers with all calculations clearly shown.(A Discussion of a Measurable Greenhouse Effect).
Thanks, Willis. Something new to learn every day!
How does the CERES data account for glancing solar, that part of insolation which reflects away from the sun?
Willis,
I really like these data presentations of yours. Kudos. The purest form of science. Simply observing what nature is telling us, then loosely ponder what it might mean and how it might fit together with other parts of the picture. More questions than answers. Let the data do the answering. There are lots of answers in just describing the data, like you do here.
It’s the preconceptions, the a priori assumptions, that end up ruining this pure form of science.
FYI, WTF:
“If an inappropriate TOA flux reference level is used to define satellite TOA fluxes, and horizontal transmission of solar radiation through the planet is not accounted for in the radiation budget equation, systematic errors in net flux of up to 8 W m2 can result. Since climate models generally use a plane-parallel model approximation to estimate TOA fluxes and the earth radiation budget, they implicitly assume zero horizontal transmission of solar radiation in the radiation budget equation, and do not need to specify a flux reference level. By defining satellite-based TOA flux estimates at a 20-km flux reference level, comparisons with plane-parallel climate model calculations are simplified since there is no need to explicitly correct plane-parallel climate model fluxes for horizontal transmission of solar radiation through a finite earth.”
Loeb, Norman G.; Kato, Seiji; Wielicki, Bruce A.
Journal of Climate, vol. 15, Issue 22, pp.3301-3309
Energy in equals energy out. No surprise.
An analogy, two houses, identical but for thickness of insulation, each heated with 5 kW.
Heat losses, through walls and roof, will be 5 kW in both houses, but inside temperature will be different.
The key question is how CO2 is affecting the “insulation” of our home. Not very much, I think.
Too bad we don’t have CERES data since 1980. Now we have to wait to get another
10-15 years data – hopefully when temperatures are going up or down. The nice stable CERES data makes sense with the relatively flat temperature profile since 2000 or so. But it will be great to have data when temperatures are not flat.
One thing that might be useful is to look at the SH albedo over time. We know the SH sea ice has been expanding and it might be interesting to see if that has been caught by CERES. My idea that it is SH sea ice that drives long term glaciation events would require some energy to be lost due to expanding sea ice.
What Kristian said.
“First, the solar input. Although a lot of folks talk about the “solar constant”, over the course of the year the sun is anything but constant.”
I understand what you mean and see that you go on to explain but on it’s own the statement seems to imply that the sun changes over the course of the year when you actually mean the amount of energy received from the sun due to orbital changes. I often see the two used interchangeably in comments which seems to confuse those that do not follow the subject closely. One should remember to make a clear distinction between the two.
Here’s an Apollo-17 Mission: December 1972 photograph to show that:-
NASA Photo ID: AS17-148-22742 File Name: 10075946.jpg
Film Type: 70mm Date Taken: 12/07/72
Title: View of the Earth seen by the Apollo 17 crew traveling toward the moon
Description:
Most of Australia (center) and part of Antarctica are visible in this photo of a three-quarters Earth, recorded with a 70mm handheld Hasselblad camera using a 250mm lens.
Subject terms:
Apollo 17 Flight Apollo Project Earth (Planet) Earth Observations (From Space) Onboard Activities Photography
Willis,
I’m also very surprised at the fig 6 results. Totally unexpected!
It would be interesting to see the same graph for june and December with only two variables. That is ; total down welling ( incoming -reflected) and total upwelling. This should show the location of the major flux transfer latitudes in the total transport system.
Mr. Eschenbach,
I always enjoy your articles, and try very hard to make time to read them — thank you for taking the time and expending the effort to research and write them.
Very much off-topic, I hope you are feeling well after your heart surgery some months back. If I may ask a question (and I hope to not be asking you to repeat something you’ve explained elsewhere that I missed): some days or weeks before your surgery, you mentioned taking a heart stress-test and knocking it out of the park with your technique of exhaling deeply — a technique I have been practicing, with some success. In retrospect, do you think that you possibly defeated the purpose of the test, in a way, as it was calibrated for more typical breathing patterns?
I ask this because I have some suspicion about medically-recommended metrics such as cholesterol, blood pressure and the obviously flawed Body Mass Index, which must be at best only rough guides, yet doctors seem to hesitate little in recommending powerful medications in the hope of adjusting observed values, with what seems to be an incomplete, at best, understanding of the underlying process. As you seem to have a flair for understanding natural processes and their measurement and modelling, I would be grateful for any insight you might care to share.
Thank you very much.
Kate
michael hammer says:
March 8, 2014 at 11:22 pm
The first post “Alcheson” makes the point – negative feedback. The variable is Earth’s temperature which adjusts itself to maintain balance between incoming and outgoing radiation. However the further point to make is that the time constant of this feedback is clearly very short – at very most a year or two (because the excursions of the residual from zero are of short duration). That means all these claims of stored up warming, ‘even if we stop emitting tomorrow the earth will keep warming for hundreds of years’ is not supported by the experimental data. That means that even if all the warming since 1950 was due to CO2 (with none due to other causes or biased adjusting of the record) the total impact of the half doubling to date is at most 0.7C. Thus the further rise in temperature by 2070 (when CO2 is predicted to be 560ppm) would be a further 0.7C. Thats a little short of the 3C predicted and clearly not in the catastrophic catagory. Of course the probability that all the warming since 1950 is due to CO2 is pretty small given that even warmists now admit to a significant “natural” component plus of course the lack of warming for the last 17 or so years while CO2 has continued to rise.
Michael, I think you are absolutely spot on here. Well said!