Guest Post by Willis Eschenbach
For a while I’ve been using a weird kind of scatter chart. Here is an example. It shows the relationship between surface temperature and the influence of clouds on surface radiation. Clouds can warm or cool the surface, depending on location, time, and type. This phenomenon is known as the “Cloud Radiation Effect” (CRE).
The amount of cloud warming or cooling radiation (CRE) is measured in watts per square meter (W/m2). Positive means that the clouds warm the surface, and negative means that the clouds cool the surface. Globally averaged by area, clouds cool the surface by about – 21 W/m2.
Figure 1. Scatter plot, surface temperature (horizontal “x” axis) versus radiative effect of real surface cloud (vertical “y” axis). Gives a new meaning to the word “non-linear”.
What are we looking at here? Well, each blue dot in Figure 1 represents a 1° x 1° longitude grid cell somewhere on the earth’s surface. Each dot is placed horizontally with respect to the 21-year average temperature and vertically along the radiative effect of its 21-year cloud. The yellow/black line is the LOW smoothness that indicates the overall trend of the data.
And of particular interest, the slope of the yellow/black line shows how variable the cloud radiation effect per 1°C changes in temperature.
From Figure 1, we can see that clouds often warm the coldest regions of the planet. The grid cells in ~10% of the planet where the mean annual temperature is below -5°C are warmed by clouds.
In warmer regions, on the other hand, clouds cool the surface. And when the temperature rises above about 25-26°C, the cooling capacity of the cloud increases sharply as the temperature increases. In those regions, for each additional temperature, the cloud’s cooling capacity increases to -15 W/m2. This is due to a rapid increase above 26°C in the number, size and strength of heat-induced thunderstorms in the humid tropics.
Here’s a video showing how thunderstorms follow warm waters throughout the year.
Figure 2. Thunderstorm intensity is shown in color (cloud top height is a measure of thunderstorm intensity). The gray outlines show the temperatures of 27, 28 and 29°C.
From this, we can see that thunderstorms emerge preferentially over hotspots, and they effectively limit how much temperatures can rise in those areas. This is the reason that only 1% of the earth’s surface area, and almost no oceans, has an average annual temperature above 30°C.
With that as a preamble, since very few people in climates use grid-based scatter plots, let me discuss this type of scatter plot. It has a very valuable property.
Value is a method that is looking at long-term averages. For example in Figure 1, this is the average temperature at which each grid cell has stabilized over millennia. Thus, the temperature of each grid cell includes all possible different responses and most of the slow responses to changing conditions.
And this allows us to answer questions like “how would the clouds react if the temperature slowly warmed up”? Alarmists would assume you believe warming will increase due to the feedback of the clouds.
But Figure 1 tells a much more complex and nuanced story. The slope of the yellow/black line shows the change in CRE in response to a 1° change in temperature. If it slopes down to the right, it shows that the intensity of cloud-induced cooling is increasing as temperatures rise — the CRE is getting more negative and the clouds are cooling more..
There are only two places where clouds work to increase warming below. These are the areas in Figure 1 where the yellow/black line slopes up to the right. They are 3% of the surface cooler than -20°C and ~30% of the earth between 15°C and 25°C. This total makes up about a third of the planet.
Gridcells at all other temperatures will have an increasing capacity for cloud cooling as they warm, particularly as the third of the globe has average temperatures above 25°C.
Inference? Only a third globally has a warming cloud response and it is not as strong. Two-thirds of the globe have a cooling cloud feedback, and in addition, the cooling feedback is much stronger than the warming feedback.
Therefore, we can say that the average cloud response is negative but not positive. The area-average of the above data shows that globally, the average cloud cooling capacity cools -3.2 W/m2 for every one degree Celsius warming. (In reality, the total response The body of the cloud will be smaller than that, because the warmest areas on earth where the response of the cloud is largest won’t usually warm up as much.)
Now, I said above that this method gives us the permanent answer after Almost all different feedback, slow heating and throttling have occurred. I have stated that this is not the short-term response of clouds to surface temperature. It’s a long-term, essentially steady-state reaction.
As a result, it could actually answer the question of the long-term response of clouds to 1°C warming. And it could answer the question in detail, showing the cloud’s response. How clouds change from the poles to the tropics.
The only argument I can see against this is that some slow thermoregulation from the most recent warming has yet to come. Maybe, but here’s why that will probably make a bit of a difference — high tides usually lift all the boats.
In other words, if we have several grid cells nearby, and one cell has a slow residual heat adjustment due to recent warming, it is very likely that other neighboring cells will also receive the same slow residual heat adjustment.
And this will leave the slope of interest, the slope of the yellow line in Figure 1, unchanged.
Or at least, that’s what my logic said. However, I always prefer data over logic. After some thought, I realized that I could test this by taking the shorter average of the CERES data instead of the full 21-year average. I used a 5-year average of the same CERES data. For comparison, I plotted them to the same scale as shown in Figure 1.
Figure 3. LOWESS smooths the scatter plots of four selected subsets of the CERES data. Scatter chart data below is not displayed.
As you can see, the LOWEST smooth trendlines of all four scatter plots of the grid are so close that they overlap each other. This certainly shows that the grid scatter plot is indeed showing a comprehensive, long-term relationship between the two variables of interest. It is largely unaffected by changes in CRE and temperature between 5-year intervals.
I will leave this here, and I will return to what I have learned from other gridcell scatterers in the next post.
My best wishes to all,
w.
PLEASE: When you comment, quote the exact words you are responding. I can defend my word. I can’t defend your repeating my words. Thank.
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