My Hypothesis Re-Emerges – Watts Up With That?

Guest Post by Willis Eschenbach

I’ve been on a most curious quest this last week.

I wanted to download all of my WUWT posts so I could make them into ebooks on various subjects. Of course, to do that I need to review some of my earliest posts. Bear in mind, this current analysis is the 1,051st post I’ve put up on WUWT, so it’s not surprising to me that I’ve totally forgotten writing some of them. This makes for an interesting journey into the past. Some of them made theoretical conjectures of mine that at the time I didn’t have the data and the computer skills and specialized functions that I have now to both do and display a whole host of data analyses in the computer language “R”.

My first post for WUWT was The Thermostat Hypothesis. A version of it was later published as a journal article named ” The Thunderstorm Thermostat Hypothesis: How Clouds And Thunderstorms Control The Earth’s Temperature.”

It’s available here, just say no thanks to the offers to join. In that post and journal article, I laid out my thoughts about how tropical thunderstorms and cumulus cloud fields worked to thermoregulate the tropical and thus the global temperature. In succeeding years, I realized that this was my first glimpse of what, down the line, I came to see as a much larger variety of individual overlapping “emergent” climate phenomena acting to oppose temperature variations.

And what is an “emergent” phenomenon when it’s at home? Let me auto-plagiarize from my post “Emergent Climate Phenomena“, which I encourage you to take a moment to read.

One common property of emergent phenomena is that they are flow systems that are far from equilibrium. As a result, they need to evolve and change in order to survive. They are mobile and mutable, not fixed and unchanging. And locally (but of course not globally), they can reverse entropy (organize the local environment). Indeed, another name for emergent phenomena is “self-organized phenomena”.

Another key to recognizing emergent phenomena is that they arise spontaneously when conditions are right. They don’t have to be artificially generated. They emerge from the background in response to local conditions (temperature, humidity, etc.) passing some threshold.

Next, they often have a lifespan. By a “lifespan”, I mean that they come into existence at a certain time and place, generally when some local natural threshold is exceeded. Thereafter, they are in continuous existence for a certain length of time, and at the end of that time, they dissipate and disappear. Clouds are an excellent example, as is our finite lifespan.

Another characteristic of emergent phenomena is that they are not cyclical, or are at best pseudo-cyclical. They do not repeat or move in any regular or ordered or repetitive fashion. Often, they can move about independently, and when they can do so, their movements can be very hard to predict. Predictions of a hurricane track are an example.

Another feature of emergent phenomena is that they are often temperature threshold-based, with the threshold being a certain local temperature difference. By that, I mean that they rarely emerge below that threshold, but above it, their numbers can increase very rapidly.

Another attribute of emergent systems is that they are often associated with phase changes in the relevant fluids, e.g. clouds occur with a phase change of water.

One final attribute of threshold-based emergent systems is crucial to this discussion—they exhibit “overshoot” or hysteresis. In the Rayleigh-Bénard circulation shown below, it takes a certain threshold temperature difference from top to bottom to cause the emergence of the circulation pattern. But once that circulation is established, it will persist even though you turn the heat down far below the initiation threshold temperature. And this kind of “overshoot” hysteresis is a requirement for successful regulation of lagged systems, system where the response to a change in inputs or conditions doesn’t occur immediately.

So those are some of the characteristic features of emergent phenomena.

• They are flow systems far from equilibrium that arise spontaneously, often upon crossing a critical threshold that is temperature-based.

• Their properties are not predictable from the properties of the condition they emerge from. There’s nothing in the atoms of water and air that would predict that they could spontaneously create lightning.

• They move and act unpredictably

• They are often associated with phase changes, and

• They often exhibit “overshoot” (hysteresis)

• They have a lifespan from their initiation to their dissolution

• Their patterns arise from many small interactions

To me, this is a two-fold explanation of why modern state-of-the-art weather models are only reliable a few days out. First, we’re dealing with emergent phenomena, and by their very nature, their future actions can’t be reliably predicted. And second, modern weather and climate models don’t feature spontaneously emerging tropical thunderstorms. They’re short on spontaneous emergence of any kind.

And that leaves them analyzing something that doesn’t exist, a world without emergent phenomena. And to attempt that analysis, they are using methods unsuited for the world that does exist, a world dominated by emergent phenomena. See my post The Details Are In The Devil for why this simply won’t work.

But I digress … reading some of these early posts, I realized that I now could further test the accuracy and understanding of the claims I made there about clouds and thunderstorms. My hypothesis back then was that clouds act to oppose both upwards and downward swings of the temperature. I said that they are a governor rather than a simple feedback.

Climate scientists talk about “cloud feedback”. But what clouds do is not just simple linear feedback of the type discussed by the IPCC scientists. For example, negative feedback just opposes warming—the warmer it gets, the more it opposes the warming.

But clouds are not like that. Clouds and thunderstorms function as a thermoregulatory governor. They don’t just slow down warming. Instead, they warm the surface when it’s cold, and they cool the surface when it’s warm.

And the reason for this post is that today I thought of another way to show that is true. Let me explain.

The CERES dataset has gridded 1° longitude by 1° latitude data of the “Surface Cloud Radiative Effect”, or “Surface CRE”, hereinafter just “CRE”. The CRE is the difference in radiation hitting the surface between when the clouds are or aren’t present. There are two kinds of cloud radiative effects, shortwave (solar radiation) and longwave (thermal radiation from the atmosphere). The net of the two is the total effect of the clouds, which depending on conditions and type of clouds can either be cooling or warming of the surface..

A negative CRE value in a given area indicates that the presence of clouds cools that part of the planet, and a positive value means the presence of clouds warms the surface.

Let me start with a global look at the surface cloud radiative effect, to determine where clouds warm or cool the surface and by how much.

Figure 1. Global surface cloud radiative effect (net of longwave and shortwave radiation). Negative is cooling, positive is warming.

This shows the net cooling effect of about -19 W/m2. But what it doesn’t show is how the surface net cloud radiative effect varies when the temperature in any location gets warmer or cooler than the average temperature for that area. That’s what the IPCC calls “cloud feedback”. They claim it is positive, meaning that when the surface warms, the clouds change to increase that warming. Me, I’ve always thought that claim was highly improbable for several reasons.

For my new way to show how clouds change with temperature, I looked by latitude at the difference between the seasonal CRE (winter and summer) and the annual average CRE. I took the global maps of mid-summer and mid-winter CRE, and from each of those I subtracted the map shown in Figure 1 above. Then I took averages by each 1° latitude band. The result is below.

Figure 2. Latitudinal by 1° bands of the difference between mid-summer/mid-winter surface CRE and the average CRE for that given latitude.

For me, the greatest joy in science is the moment of seeing graphically the result of some new, unknown analysis method. And in this case, I didn’t expect the outcome. The difference in cloud radiative effect is large, regular, and stark. The difference between mid-summer and mid-winter CRE is up to 110 W/m2 in the northern hemisphere and 160 W/m2 in the southern hemisphere.

In addition, in mid-summer in both latitudes the clouds on average cool the entire summer hemisphere compared to average conditions, pole to Equator. And the opposite is true. In mid-winter, clouds in the entire winter hemisphere show increased warming compared to annual averages.

In other words, compared to the local annual average values, when it is warmer than usual, clouds cool the surface, and when it is cooler than usual, clouds warm the surface.

Encouraged by this finding, I decided to look at those summer/winter changes versus annual averages as maps of the earth.

Figure 3. As in Figure 2, showing summer and winter changes from the average conditions.

This gives us another view of how the surface cloud radiative effect changes with the seasons. In winter it gives extra warming, and in the summer it gives extra cooling.

The scientific test of a hypothesis is whether it makes successful, testable predictions. The figures above verify exactly the testable prediction based on what I hypothesized fifteen years ago, that clouds and thunderstorms warm the earth when it is cool and cool it when it is warm … what’s not to like?

My best to all, and please … no personal attacks.

w.

As I’ve said before … when you comment, please quote the exact words you are discussing. It avoids endless misunderstandings and disputes.