Bad Science: Estimating Cloud Feedback Using CERES Data

Guest Post by Willis Eschenbach

As usual, Dr. Judith Curry’s Week In Review – Science Edition contains interesting studies. I took a look at one entitled “Cloud feedback mechanisms and their representation in global climate models“, by Ceppi et al., hereinafter Ceppi2017. The paper looks at the changes in the radiative effects of clouds. From the paper:

The radiative impact of clouds is measured as the cloud-radiative effect (CRE), the difference between clear-sky and all-sky radiative flux at the top of atmosphere. Clouds reflect solar radiation (negative SW CRE, global-mean effect of −45 W m−2) and reduce outgoing terrestrial radiation (positive LW CRE, 27 W m−2), with an overall cooling effect estimated at −18 W m−2 (numbers from Henderson et al.[36]).

The Ceppi2017 Figure 1 shows that almost all of the models report that as the modeled surface warms, the modeled clouds change in such a way as to increase the modeled warming. On average, Figure 1 shows that for every degree C that the modeled surface warms, the modeled clouds add on another ~ 0.5 W/m2 of additional modeled forcing.

cloud feedback cepp1 fig 1

Figure 1. First figure in Cepp1017, as detailed in their caption.

Let me say that I find such a large positive cloud feedback to be very doubtful. Setting that aside for the moment, the Ceppi2017 authors have included a graphic showing the average change in the modeled cloud radiative effect from a number of models.

cloud feedback ceppi fig 3

Figure 2. Average modeled net cloud feedback, from Ceppi2017.

I thought, hmmm … I wondered how that compared to the CERES data. Here’s a look at the same thing, net cloud feedback … except theirs is modeled and the CERES satellite data below is observational.

net cloud feedback CERES

Figure 3. Average of 180 months of CERES data showing the relationship between changes in temperature and corresponding changes net cloud feedback. The calculations are done on a gridcell by gridcell basis, with the monthly gridcell climatology removed before the calculations.

Now, while the models kind of get it right, there are several problems with them. In the CERES data above, you can clearly see the Inter-Tropical Convergence Zone (ITCZ) as the yellow/green area above/below the equator in the Atlantic, Pacific, and Indian Oceans. In the models, it is only weakly visible in the Atlantic and is missing in the Pacific and Indian Oceans.

Next, the CERES data shows that much more of the planet has negative net feedback than the models claim. The entire southern extra-tropics shows negative cloud feedback, some of it quite strong.

Next, because theirs is an average of various models, it doesn’t capture the full variation in the net cloud feedback. In the real world, there are areas of both strong positive and strong negative feedback.

Finally, on average the CERES data shows that the net cloud feedback is negative. Now, we have to take the accuracy of that number with a grain of salt, in that we are looking at trends. Trends are a ratio, and ratios tend to distort averages. For example, the area-weighted average of the trends as shown in Figure 3 is -1.4 W/m2 per °C. A better measure is likely the area-weighted median of the trends, which is -0.5 W/m2 per °C.

Alternatively, we can look at the relationship on a global basis. Here’s a scatterplot of the monthly residual changes in CRE versus the monthly residual changes in temperature (after removing the global monthly climatology).

scatterplot cre vs temperature

Figure 4. Scatterplot of the monthly global CRE and temperature data.

This gives us a third estimate of the relationship between CRE and temperature. This one is between the other two; we have estimates of the cloud feedback factor of -1.4, -1.0 ± 0.3, and -0.5 W/m2 per °C

Whichever way we estimate it, however, the CERES data shows that the net effect of clouds is negative, not positive as the models claim. The average of the models’ estimates of cloud feedback is about plus one-half of a W/m2 per °C. The CERES data, on the other hand, gives a value of about minus one W/m2 per °C.

This is a net swing on the order of ~ 1.5 W/m2 per degree C between the model estimates and the CERES data … and thus a 1.5 W/m2 reduction in the estimated climate sensitivity.

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Let me say in closing that I don’t think that “climate sensitivity” is a real thing. I say this because of ample evidence that the climate is a governed system, with a variety of thermoregulatory climate phenomena that work together to constrain the global temperature to a very narrow range (e.g. ± 0.3°C variation over the entire 20th Century). When such a system is in a steady state like that of the earth, the temperature is essentially decoupled from the “forcing” (the changes in downwelling radiation). And because it is decoupled, there is no such thing as “climate sensitivity”.

Ref.: https://wattsupwiththat.com/2017/05/25/estimating-cloud-feedback-using-ceres-data/

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