Guest Post by Willis Eschenbach
Every fall, there’s good news in the world of satellite information, because the CERES satellite folks add one more year’s worth of data to their full dataset. So I went and downloaded the whole 18 years worth, which is close to a full gigabyte of data …
The other good news is that even though I live out in the country, the neighbors all got together a year ago and got a grant from the state to install fiber-optic lines to all of our houses. Last year I was on satellite internet, with a ping time of ~800 msec and about 10 Mbps upload and download. Here’s where I am today, one happy data junkie … go figure.

But I digress … I decided to take a look at the relationship between the top-of-atmosphere (TOA) total radiation imbalance and the surface temperature. Figure 1 shows how the Northern Hemisphere temperature varies with respect to the TOA imbalance.

Figure 1. Scatterplot, monthly Northern Hemisphere surface temperature versus the monthly TOA imbalance. Temperature is in degrees Celsius (°C), and TOA imbalance is in watts per square metre (W/m2).
The oval shape of the relationship indicates that there is a lag between the change in the TOA radiation and the temperature, as we’d expect. Figure 2 shows the same scatterplot after lagging the relationship by one month.

Figure 2. Scatterplot, monthly Northern Hemisphere surface temperature versus the monthly TOA imbalance lagged by one month.
I’ve indicated in the top left the “Instantaneous Climate Sensitivity”. This is the immediate system response to a change in the TOA radiation.
Now, in a post from three years ago called “Lags and Leads” I discussed how to determine the true size of the response if there were no lag between the forcing and the response. A more detailed calculation of the data in Figure 2 shows a lag of 34°. Using the equations given in that post, it gives us the no-lag climate sensitivity shown in the lower right of Figure 2, which is about a tenth of a degree for each additional watt per square meter (W/m2) of TOA radiation.
So far, so good. Here’s where it gets interesting. Suppose we remove the average repeating monthly variations (called the “climatology”) in both datasets, leaving just the anomalies. What would we expect to find?
Well, we’d expect to find that the temperature anomalies would vary as a linear function of the TOA radiation anomaly. In a perfect world, it would look like Figure 3.

Figure 3. Scatterplot, expected value of the Northern Hemisphere temperature anomaly versus the NH TOA imbalance anomaly in a perfect world. The slope is the slope of the data shown in Figure 2.
Of course, however, things are not perfect. So let me add some errors to the expected perfection. I’ve used random normal errors with a standard deviation equal to that of the actual temperature anomaly. Figure 4 shows the result in Figure 3 plus a random error applied to each data point.

Figure 4. Scatterplot, expected value of the NH temperature anomaly versus the NH TOA imbalance anomaly plus random errors.
Note that the addition of the errors doesn’t remove the statistical significance. The p-value is vanishingly small. Nor does adding the errors significantly change the calculated slope of the relationship between the temperature anomaly and the TOA imbalance anomaly.
So … what do we find when we look at the actual data? Curiously, we find no relationship at all between the temperature and the TOA anomaly.

Figure 5. Scatterplot, expected value of the NH temperature anomaly versus the one-month lagged NH TOA imbalance anomaly.
As you can see, there is no relationship between the temperature anomaly and the TOA radiation anomaly.
So … why is there no relationship as we might expect? It’s because of the thermostatic action of the emergent phenomena. Let me explain using a familiar example of a thermostatically regulated system—a house with a thermostat controlling a furnace.
Let’s suppose that it’s cold outside. We go out for a while, so we turn the thermostat down to say 50°F (10°C). After a while, the house cools to that temperature and then remains there. When it gets cooler, the furnace kicks in and heats it up to slightly above the thermostat setting. Then it turns off.
Now, let’s suppose we come home, walk in the door, and kick the thermostat up to say 70°F (21°C). The furnace comes on, and the house starts to heat.
As the house is heating up to the 70°F setting, we can calculate the incoming energy as the amount of heat put out by the furnace per minute. Then we can compare it to the change in temperature per minute. This is the equivalent of comparing the change in global surface temperature to the change in the TOA radiation imbalance. We get an answer showing the amount of temperature change in the house versus a given change in incoming energy.
But once the temperature of the house reaches 70°F, a funny thing happens. The temperature of the house totally decouples from the amount of incoming energy. If the day is cold, the furnace will run a lot to keep the house at that temperature setting … but if the day is warm, the furnace will hardly run at all to keep the temperature at the setting. And as a result, there is no longer a fixed relationship between temperature and incoming energy.
So in the same system, we have some situations where the temperature is related to the incoming energy, and other situations where the temperature is totally decoupled from the incoming energy.
Similarly, when the Northern Hemisphere goes from say the August temperature setting to the September setting, we get a clear, albeit small, relationship between temperature and TOA energy input.
But when we look at what happens when we isolate and just look at say the September setting, we find that there is no relationship between temperature anomaly and TOA energy input anomaly.
Anyhow, that’s what I noticed while looking at the latest CERES data …
Here, we’ve had about four inches (10 cm) of rain in the last two days. This has washed all of the forest fire smoke out of the atmosphere. And last night, we had a wonderful Thanksgiving dinner chez nous, featuring the usual cast of inlaws and outlaws, seventeen in total. No, we didn’t discuss politics, although I’m sure if we had I could have saved big money on Christmas presents … instead, we laughed and told family stories and caught up on what we’ve been doing since last year at this time.
And just like every Thanksgiving,

But what’s a poor boy to do, the food was amazing.
My very best Thanksgiving wishes to all,
w.
My Normal Request: When you comment, please quote the exact words you are discussing so we can all be clear who and what you are referring to.
Willis – I vaguely remember that raw CERES data used to show an unexplained imbalance of 5 W/m2. Did they successfully homogenize it out of existence?
Good question, George. They have always adjusted the figures to give a net TOA imbalance on the order of 0.7 W/m2 … which means that you can’t trust it for absolute numbers, but it is very good for relative numbers. High precision … lower accuracy.
w.
Willis:
You were looking at various times for cycles and solar variations and possible connections.
See Table 2 in
Climate Forcing by Changing Solar Radiation
by Lean and Rind, respectively from NRL and GISS.
https://journals.ametsoc.org/doi/pdf/10.1175/1520-0442%281998%29011%3C3069%3ACFBCSR%3E2.0.CO%3B2
They name 15 groups of cycles, and 54 detectable ‘periods”.
That compares the Lean TSI reconstruction, which even she has deprecated, and the old sunspot numbers, which have been corrected since 1998 when the paper was published, with a variety of climate data that they claim has the following cycles in years:
3–5, 3.1, 3.3, 3.4, 3.5, 3.8, 4.5, 4.6, 5, 5.2, 6, 7, 7.5, 8, 9, 9.3, 9.9, 10, 10–12, 11, 11.3, 14.5, 14.8, 17, 18, 18.4, 18.6, 18.7, 20, 22, 23, 27, 34, 51.3, 56, 76, 77, 84, 126, 133
Ya think they might find something in there? That’s called a “data trawl” …
In any case, I looked at one of the datasets they discuss, the Southern Oscillation, because I happen to have the data for the Southern Oscillation Index. They say it shows cycles of 3, 3.8, 6, 10–12, 22, and 34 years, plus the 2.2 year QBO cycle. Here’s a periodogram of the SOI:
As you can see, this clearly shows the 2.2 year QBO cycle. It also shows cycles at 3.8, 5.8, 6.5, 9, and 13.8 years. It does NOT show the claimed cycles at 3, 6, 10-12, 22, or 34 years.
This is a recurring problem. People claim to see cycles in the data that don’t exist.
Note also that NONE of the cycles in the SOI are particularly strong, with none of them being larger than 8% of the peak-to-peak swings of the data.
Best regards,
w.
Willis, nice to see such large datasets flipped around with apparent ease 🙂
Though I think that you have taken a bit of a wrong turning towards the end: When you did the simulation of adding some error it looks as if you only added the error to the Y term [temperature anomaly] . If random errors are added to the X term of an OLS regression then the slope and the significance declines.
I think that adding some noise to both the X and Y terms would be the right analogous situation (?)
Thanks for the kind words, Chas. The “apparent ease” is the result of two things.
One is that I’m writing code to analyze the data in the computer language “R”, which is far and away the easiest language to use for large datasets. The other is that I’ve written a whole host of functions that I reuse. The CERES data is all in large arrays, with x being longitude, y being latitude, and z being time. Each layer is a month. So I have functions to get weighted means of the arrays, or trends, or monthly average values, or a number of other common tasks.
Then I have a function called “drawworld” which takes the array, averages it by gridcell, and plots it with the Molleweide projection. At this point I’ve already done most of the heavy lifting to write the functions, so I just need to call them up as required.
Regarding errors, the “x” term is the time of the observations. We know that time down to the nearest millisecond. So adding error to the “x” term doesn’t seem reasonable to me.
Regards,
w.
I wanted to comment on the following exchange:
Curious George November 24, 2018 at 11:11 am
Reply
Crispin in Waterloo November 24, 2018 at 2:01 pm
This is kinda true. The instantaneous TSI varies over the course of the year. But here’s the curious part.
Although the earth receives a greater TSI when it is closer to the sun … it is also moving faster when it is closer to the sun.
And although the earth receives a lesser TSI when it is further from the sun … it is also moving slower when it is further from the sun.
And because both gravity and TSI fall off as the square of the distance from the sun, these two cancel each other out exactly. So the net result is that over the course of the year both hemispheres receive exactly the same amount of of solar radiation.
This speed difference is visible in the length of time between equinoxes:
Vernal Equinox to Autumnal Equinox 186.4 days
Autumnal Equinox to Vernal Equinox 178.9 days
As you can see, the earth is moving more slowly during the NH summer, and more rapidly during the SH summer.
Ain’t physics wonderful?
Sadly, this makes experiments of the type proposed by Crispin in Waterloo above unrevealing … because although the points on the equator get more instantaneous
insolation during part of the year, they get it for less time, so the net insolation is unchanged.
Regards to all,
w.
detailed calculation of the data in Figure 2 shows a lag of 34° –>
detailed calculation of the data in Figure 2 shows a lag of 0.34°
/ or what I’m missing /
Johann, the lag is a little bit more than one month out of the twelve month solar heating cycle. If it were one month exactly out of twelve, that lag would be 1 month / 12 months * 360° = 30°.
w.
My fault – correct me wrong!
Regards – Hans
/ don’t know the book before the last page /