The Daily Albedo Cycle

Guest Post by Willis Eschenbach

I discussed the role of tropical albedo in regulating the temperature in two previous posts entitled Albedic Meanderings and An Inherently Stable System. This post builds on that foundation. I said in the latter post that I would discuss the diurnal changes in tropical cloud albedo. For this I use a marvelous dataset called the TAO dataset. It is measurements from a number of moored buoys in the tropical Pacific.

Figure 1. Locations of all of the TAO buoys ever in operation. Background shows the sea surface temperature.

Sadly, despite the billions spent on “global warming”, the TAO buoys don’t have funds for maintenance. As a result, the records from some have ceased entirely. But I digress … the great thing about the TAO buoy records is that they are either hourly, or every ten minutes, or even every two minutes in some cases. This lets us accurately reconstruct the daily cycles.

To refresh your memory, my hypothesis is that variations in the timing and strength of the emergence of tropical cumulus and tropical thunderstorms act to regulate both the amount of incoming energy and the tropical surface temperature. I say that whenever there is a hot day or a hot area, we get earlier and more dense cumulus and thunderstorms. The cumulus clouds act solely by reflecting the sunlight. Thunderstorms, on the other hand, cool the surface in dozens of ways. This prevents the surface temperature from overheating.

So with that hypothesis in mind, let me start by looking at the daily air temperature cycles. Because of availability of data, I’ve used data from a string of buoys along the Equator. The buoys I used stretch from 95°W (buoy just to the left of the “E” in “Equator”) to 165°E (on the Equator northeast of Australia). Conveniently, the average temperature increases steadily along the line. Figure 2 shows the daily variations in surface air temperature for those Equatorial buoys:

Figure 2. Average daily air temperatures measured at ten minute intervals at eight different buoys. Colors represent temperatures.

Using just locations along the Equator gives me a peculiar advantage. All of the locations receive exactly, precisely, the same amount of top-of-atmosphere solar energy every single day. This means that the differences between them can’t be from different solar forcing. It eliminates a variable from the equation.

Now, there is an oddity about these records, which no doubt you’ve noticed. The temperature doesn’t warm steadily during the day. Let me show you what I mean. Here’s a chart I made a while back showing temperatures at Santa Rosa, California, the met station nearest to where I live.

Figure 3. Hourly temperatures averaged over a year in Santa Rosa, CA. About 20 miles (30 km) from the ocean. The photo shows wine grape trellises.

As you might expect in a generally marine climate that usually doesn’t get much in the way of afternoon clouds or thunderstorms, the graph is simple. As the solar energy increases the earth warms. It continues to warm until around 2:00 and then starts to drop. It cools rapidly at first, then more slowly towards early morning.

However, that’s not the pattern we saw in Figure 2. Instead of a steady straight rise from dawn to noon, there is a bend or a “dip” in the rate of temperature rise. This can be seen more clearly when we look at the same records shown in Figure 2 as anomalies (variations about their individual averages). Figure 4 shows the same data as in Figure 2, but with each individual average subtracted from its respective record.

Figure 4. Same data as in Figure 2, but expressed as anomalies about the individual means (averages). Colors indicate buoy average temperature as shown in Figure 2.

Here we see a most interesting progression. The cyan (light blue) colored trace of 95°W, the coolest buoy, shows only a slight bend in temperatures from 6 am to the afternoon peak. It’s nearly straight. But as we look at warmer and warmer buoy locations, the bend becomes more and more pronounced. In the warmest five locations, there is an actual “dip”, a reduction in temperature as the day progresses.

In addition, the peak temperature anomalies start decreasing with warmer temperatures. Since there is identical solar input to all of the buoys, this must reflect some local phenomenon.

To me, the “dip” in the morning records is the clear sign of the phenomenon I described in my last post—the emergence of the cumulus clouds starting in mid-to-late morning. Through variations in their emergence time, as soon as a certain temperature threshold is surpassed these clouds “throttle” the incoming solar energy by reflecting some of it back to space. This cloud throttling effect is so strong and comes on so suddenly that in the warmer locations, the temperature actually drops despite the continually increasing morning sunshine.

However, in no case is the throttling effect of the morning albedo change sufficient to overcome the full strength of the tropical sun. This is because there is no way for these cumulus to cover the entire sky—there needs to be clear descending air around each cumulus cloud to maintain circulation. As a result, there is only so much the cumulus reflections can do … and so past noon the day continues to warm. The later reduction of the peak afternoon temperature values is due not to increased albedo but to the emergence of afternoon thunderstorms. These “chop the top” off of the temperatures, imposing a high temperature limit and preventing further surface temperature rise.

Having seen that, let me move on to another way that we can see the effect of the morning-time cloud albedo. Note that the clouds that create the reflective albedo which helps regulate the tropical temperature only emerge in response to the surpassing of a temperature threshold. Once that threshold is passed and the increased cloud albedo has come into existence, it acts to reduce the high temperatures by cutting way back on the incoming solar energy.

Given the nature of the regulation, which depends on reflecting the sun’s rays, we can make the following predictions.

• The regulation of the temperature will be stronger in the day than in the night. No sun, no reflection …

• The regulation of the temperature will be greater in the morning than the afternoon. This is because the early morning is often clear and the late morning is cloudy, whereas there are generally clouds throughout the afternoon. As a result, controlling the onset time of the cloud formation will provide powerful regulation, and generally that happens in the morning.

• The regulation of the temperature will be greater up at the warm end of the scale than down at the cool end. This is because the emergent phenomena act to reduce peak temperatures.

With those predictions in mind, I cast around for some way to visualize the effects of the thermal regulation due to clouds and thunderstorms. Figure 5 shows my solution. It is the record of the hourly air temperature from the TAO buoy on the Equator at 165 East. This is the warmest of the buoys in the graphs above (red line in those graphs).

Figure 5. Boxplots of the hourly air temperatures at 0N165E. There are 59,429 observations, or about 2,500 for each hour of the day.

A “boxplot” gives various information about the distribution of the data, including outliers. The green boxes show the range that contains half of the data (the “interquartile range” or IQR). The heavy black line is the median of the data, which is the point with half the data above it and half below. The dotted “whiskers” show a distance from each green box of 1.5 times the IQR for that data. Black crosses show “outliers”, which are data points that are further from the boxes than the extent of the whiskers.

An examination of Figure 5 shows that the predictions of the distributions are borne out by the data. First, daytime regulation, from 6 AM to 6 PM (18:00 hours), is much stronger than night-time regulation. Daytime temperature regulation is so strong that there is not one single outlier on the warm side from dawn until noon, and only one (or in one instance two) outliers in each hour from noon to sunset. In fact, daytime regulation is so strong that there are many night-time temperatures that are greater than the record noon-time temperature … go figure.

Second, the regulation is stronger in the morning than the afternoon. The variations in the timing of the albedo changes are able to oppose the sun successfully until about noon (see Figure 4). After that, the continued solar input starts driving the temperature higher, and the regulation is not as certain.

Third, it is clear from the number and distribution of the outliers above and below the row of boxes that there is extensive downward pressure on any warm temperatures. This shows the cloud/thunderstorm control system is pushing back at the hot spots, cooling them down. Nor does this downward pressure only exist on the warmest temperatures. A close examination of the location of the median line shows that the median is in the middle of the green box from midnight to dawn. But during the day, the median is high up in the green box, showing that downwards pressure from the regulatory mechanisms extends well down into the body of the data.

My conclusion is that this downward pressure is the combination of cumulus clouds throttling back solar input in the morning, and thunderstorms and squall lines moving heat from the surface to up near the tropopause in the afternoon. It is this regulation of each day’s maximum tropical temperature via a host of inter-related mechanisms that keeps the earth from overheating on a daily basis.

And as I mentioned in my previous post, my insight was that if there are mechanisms that reliably keep the earth from overheating for a single day, they would keep the earth from overheating for a million years …

I may return to these topics in a future post, I’ve only scratched the surface of the TAO data.

My best wishes to each of you,

w.

My Customary Request: If you disagree with someone, please quote the exact words you disagree with. That way, everyone can understand your objection.

Data and Code: I’ve been wrestling this for too long, I’m burnt. I’ll post up the code when I get time if someone wants it. This code a dog’s breakfast, no order, functions used before they’re defined, sections of dead code exploring blind alleys. The data, on the other hand, is from the TAO website.