Wrong Again …

Guest Post by Willis Eschenbach

Like anyone else, I’m not fond of being wrong, particularly very publicly wrong. However, that’s the price of science, and sometimes you have to go through being wrong to get to being right. Case in point? My last post. In that post I looked at what is known as “net cloud radiative forcing”, and how it changed with surface temperature. Net cloud forcing is defined as the amount of downwelling upwelling longwave radiation (ULR, or “greenhouse radiation”) produced by the cloud, minus the amount of solar energy reflected by the cloud (upwelling shortwave radiation, or USR). If net cloud forcing is negative, it cools the earth below.

I found out that indeed, as temperature goes up, the net cloud radiation goes down, meaning the clouds have a greater cooling effect. I posted it, and asked for people to poke holes in it.

What could be wrong with that? Well, I forgot a very simple thing, and none of the commenters noticed either. The error was this. Net cloud forcing is cloud DLR ULR minus shortwave reflected by that same cloud. But what I forgot is that reflected shortwave is the cloud albedo times the total insolation (downwelling solar shortwave radiation).

The catch, as you probably have noticed, is this. If the cloud doesn’t change at all and the total insolation rises, the net cloud forcing will become more and more negative. The upwelling reflected solar is the cloud albedo times the insolation. As insolation rises,  more and more sunshine is reflected, so the net cloud forcing goes down. That’s just math.

The problem is that as insolation rises, temperatures also rise. So by showing net cloud forcing goes down with increasing temperature, all I have done is to show that net cloud forcing goes down with increasing insolation … and duh, the math proves that.

However, recognizing that as the problem also gave me the solution. This is to express the net cloud forcing, not as a number of watts per square metre, but as a percentage of the insolation. That way, I could cancel out the effect of the insolation, and extract the information about the clouds themselves. Figure 1 shows the results of that analysis.

Figure 1. Net Cloud Forcing (W/m2) as a percentage of gridcell insolation (W/m2), monthly averages from 1985-1989. Average percentage results shown above each map are area-averaged. Missing data shown in gray. Cloud forcing data from ERBE, insolation data from NASA.

This is an interesting result, for a variety of reasons.

First, it is quite detailed, which gives me confidence in the geographical accuracy of my calculations. For example, the cooling effect of the thunderstorms in the Inter-Tropical Convergence Zone (ITCZ) is clearly visible in the Pacific as a horizontal blue line slightly above the equator, and can be seen in the Atlantic Ocean as well. The ITCZ is the great band of equatorial thunderstorms around the planet that drive the Hadley circulation. Remember that the majority of the  energy entering the climate system is doing so in the Tropics. Because of that, a few percent change in the equatorial net cloud forcing represents lots and lots of watts per square meter.

Second, the differing responses of the clouds over the land versus clouds over the ocean are also clearly displayed. In general, land clouds warm more/cool less than ocean clouds. In addition, you can see that while the clouds rarely warm the NH ocean, they have a large warming effect on the SH ocean.

Third, and most significant, look at the timing of the seasonal changes. Take December as an example. In the Northern Hemisphere this is winter, the coldest time of year, and the clouds are having a net warming effect. In the Southern Hemisphere summer, on the other hand, clouds are cooling the surface. But by June, the situation is reversed, with the clouds having a strong cooling effect in the warm North, while warming up the winter in the South. (Note that the NH warming effect is somewhat masked by the fact that there are large areas of missing data over the land in the NH winter, shown as gray areas. The effect of this on the global average is unknown. However, by using a combination of gridcells which are adjacent temporally and gridcells which are adjacent spatially, it should be possible to do an intelligent infill of the missing areas and at least come to a more accurate estimate of the net effect. So many paths to investigate … so little time.)

I have hypothesized elsewhere that the earth has a governor which works to maintain a constant temperature. One of the features of a governor is that it cannot be simple fixed linear feedback. By that, I mean it must act in two directions—it must act to warm the earth when it is cold, and to cool the earth when it is warm. This is different from linear negative feedback, which only works to cool things down, or linear positive feedback, which only works to warm things up. A governor has to swing both ways.

Figure 1 clearly shows that, as I have been saying for some time, including both the longwave and shortwave effects clouds act strongly to warm the earth when it is cold (red areas in Figure 1) and to cool the earth when it is warm (blue areas in Figure 1). In addition, as I have also said (without much evidence until now to substantiate my claim), the ITCZ has a large net cooling effect.

So that’s where I am up to right now in my investigation of the ERBE data. Always more to learn, I’ll continue to report my results as they happen, the story of the ERBE data is far from over. I’ll be in and out of contact for a bit, I’m around today but I’m hitchhiking up to Oregon tomorrow for a friend’s bachelor party, so don’t think I’m ignoring you if I don’t answer for a bit.

w.

PS – there are some interesting results that I’ll post when I have time. These involve looking at the phase diagrams for cloud forcing, temperature, and insolation. Having the insolation available allows the phase of both the temperature and the forcing to be compared to what is actually the underlying driving mechanism, the insolation.

Regarding temperature and insolation, the ERBE data shows what is well known, that the temperature changes lag the insolation changes by about two months in the Southern Hemisphere, and by one month in the Northern Hemisphere. This is because of the thermal inertia of the planet (it takes time to warm or cool), along with the greater thermal inertia of the greater percentage of ocean in the south.

The interesting part is this: the phase diagram shows that there is no lag at all for the changes in the clouds. They change right in step with the insolation, in both the Northern and Southern Hemispheres.

This means, of course, that the clouds move first, and the temperature follows.

I’ll post those phase diagrams when I have some time.

[UPDATE: The phase diagrams, as mentioned. First, Figure 2 shows the temperature versus the insolation:

Figure 2. Insolation vs absolute temperature, from the equator to 65 N/S. The poles are not included because the ERBE cloud data only covers 65 N/S. This does not affect the phase diagrams. Black line shows no lag, gold line shows one month lag, red line shows two months lag between maximum insolation and maximum temperature. Numbers after month names show months of lag.

Since the driving signal (insolation) peaks in June and December, those months will be in the corners when the two cycles are aligned. In the Northern Hemisphere (upper panel), December is in the lower left corner with a lag of 1 month (gold line).

The Southern Hemisphere is half a cycle out of phase, so December is maximum insolation in the upper right corner. This occurs with a lag of two months (red line).

This verifies that temperatures lag insolation by a month in the Northern Hemisphere (the warmest time is not end June, when the insolation peaks) and two month in the southern hemisphere.

However, the situation is different with the clouds, as Figure 3 shows.

Figure 2. Insolation vs cloud forcing %, from the equator to 65 N/S. The poles are not included because the ERBE cloud data only covers 65 N/S. I suspect that the odd shape is a consequence of the missing gridcell data in the ERBE dataset, but that is a guess.

For the cloud forcing in both Hemispheres, there is no lag with regards to the insolation.

w.