Lord Christopher Monckton is a talented mathematician, and there are many things on which we agree. But it is unhelpful to the skeptical response to claims of a supposed climate emergency to be chasing rabbits down holes when others have already gone on that chase. So, what follows is my latest attempt to explain why Monckton’s feedback arguments supporting a very low climate sensitivity cannot be supported. This doesn’t mean his conclusion is wrong, only the line of reasoning that led him to that conclusion.
Couched in the obscure language of feedback analysis, and the mathematical gymnastics deriving from initial assumptions regarding those feedbacks, Lord Monckton’s latest explanation of his climate feedback theory (Why It Matters That Climatologists Forgot the Sun Was Shining) tends to skirt around actual physical processes. For if he were to actually investigate what meteorologists and climate scientists already know of atmospheric processes, he would not still be pushing his current theory.
Here I will try to explain, based upon actual atmospheric processes, why his argument does not make physical sense.
Christopher’s latest installment explaining his logic begins (emphasis added),
It is now almost two years since we submitted our paper on the central error perpetrated by climatologists in their attempts to derive climate sensitivity to anthropogenic greenhouse-gas forcings — namely, their failure to appreciate that such feedback processes as subsist in the climate system at any given moment must, at that moment, necessarily respond equally to each Kelvin of the entire reference temperature. Feedbacks do not, repeat not, respond solely to perturbation signals, the reference sensitivities. They also respond to the base signal, the emission temperature that would prevail even if there were no greenhouse gases in the air, because the Sun is shining.
I cannot emphasize enough just how bold (and wrong) the underlined assertion is. The idea that the climate system’s response to a small perturbation from its current state might be discerned from its response to the presence of solar heating assuming a theoretical initial cold Earth is not new, but was rejected many years ago based upon the known behavior of clouds and the atmospheric circulations associated with them.
The issue is not unlike the Ramanathan and Collins (1991) “cloud thermostat” hypothesis, which imagines that just because the Pacific Warm Pool is limited in its warmth by local cloud formation, that global warming will be limited by even more cloud formation. Hartmann and Michelsen (1993) and Lau et al. (1994) quickly responded to that claim by pointing out that vertical circulations created in the cloudy air must also produce descending, clear air elsewhere. Thus, more clouds on one region can actually cause fewer clouds elsewhere. This shows than even an expert in atmospheric radiative transfer (Ramanathan) could be misled without an adequate understanding of atmospheric circulation systems.
I’m not claiming that further warming of the climate system won’t be mitigated by an increase in clouds, as Monckton’s analysis implies. Just that we cannot get to that conclusion from the evidence presented.
Yes, Clouds Cool the Climate System
It has long been known that clouds, on average, cool the climate system. Sunlight heats the surface of the Earth, which combined with the atmospheric destabilization from the greenhouse effect, leads to convective heat transport away from the surface. Due to the presence of water, clouds form, reflecting sunlight back to outer space. While those clouds also enhance the water vapor-dominated greenhouse effect, the solar reflection (albedo) effect dominates, leading to the observation that clouds, on average, cool the climate system.
So, it might seem logical to assume (at least as a starting point) that any additional source of heating (positive energy imbalance) would lead to even more clouds, and thus a negative cloud feedback. As far as I can tell, this is the physical underpinning of Monckton’s argument. Of course, clouds might not be the only element of his argument, but clouds are arguably the most prominent example.
The trouble is that when clouds form, most of them are embedded in ascending air currents. All of that ascending air must be exactly matched by an equal amount of descending air, which is almost always cloud-free.
Thus, one cannot create more clouds without creating more clear air. When you experience a cloud-free day, it’s because ascending cloudy air with precipitation, hundreds of miles away, is forcing the air over you to sink. This is why cloud feedbacks are so uncertain, and why we cannot use the average base-state response of the climate system to the presence of sunlight to estimate climate sensitivity.
Another way to express this is that the climate system’s response to solar heating is non-linear. Initial warming from a base state of a cold, dark Earth to a solar heated one is to create clouds (a cooling effect), but the resulting vertical air circulations means you cannot created an ever more cloud-covered Earth with ever more heating. Descending air currents in response to rising air currents will not allow it.
Even Climate Models Tell Us This is the Case
Like weather forecast models, modern 3D climate models deal with the equations of motion, conservation of mass, energy, moist processes, and the atmospheric equation of state. In other words, they depend upon physics. (This does not mean all of those physical processes — especially cloud microphysical processes — are sufficiently well known to allow useful predictions of future average climate states. I don’t believe they are. My point is that the models depend upon our knowledge of the physics of a wide variety of complex processes.)
If you start-up a computerized climate model from an initial cold state (pick any cold temperature you want, say 50 Kelvin), with no clouds, the modeled system will warm, clouds will form, and the system will eventually reach a state of quasi-equilibrium, with the global area-average rate of absorbed solar energy equaling the average rate of infrared cooling to outer space. These results are consistent with the statement that “clouds cool the climate system”.
But if a small energy perturbation is then added (e.g. from more CO2 in the atmosphere reducing the rate of IR cooling, or from increasing the intensity of sunlight), clouds in the model will often respond by being reduced, not increased, in response to the small CO2-induced warming. Years ago we did this experiment with a limited-domain version of the ARPS cloud-resolving model. Global climate models would do the same thing.
The cloud response to the perturbation is not prescribed by the modelers as a cloud feedback. It is the result of the physics (and cloud microphysics) in the model. Climate model feedbacks are not prescribed; they are diagnosed after the model is run from model output.
I’m not claiming cloud feedbacks are negative or positive. Only that you cannot use the observation that “clouds cool the climate system” as a basis for determining cloud feedbacks in response to adding more CO2 to the atmosphere. And, as far as I can tell, this is the physical assumption Monckton makes in his feedback-based arguments.
Climate Sensitivity Does Not Depend Upon Feedback Analysis
For better or worse, Jule Charney and his co-authors in 1979 decided to use the forcing-feedback paradigm to explain the response of the climate system to increasing CO2. As a result, some climate skeptics have seized upon the lack of a direct one-to-one correspondence between feedbacks in electrical circuit design and climate feedback analysis. But the use of the forcing-feedback paradigm was simply a way for climate researchers to explain, in conceptual terms, how the climate system responds to an imposed energy imbalance.
While this paradigm has been useful (even quantitatively), the sensitivity of modern 3D climate models does not depend upon feedback analysis, per se. One could talk about sensitivity kernels or other plain-language terms for the partial derivatives without using the f-word. The feedback concepts which Lord Monckton imagines the climate system depends upon are only used by climate modelers as a simple way to conceptually describe the behavior (output) of climate models: that for an imposed energy imbalance in the climate system, a certain amount of warming takes place after all temperature-dependent adjustments (e.g. cloud and water vapor changes in response to warming) in the system occur. These temperature-dependent responses (feedbacks) either amplify (positive feedback) or reduce (negative feedback) the direct warming effect from the imposed energy imbalance. (Remember, almost without exception, the temperature change in anything is the result of energy imbalance).
Now, it is true that feedbacks in the models are indeed quantitatively diagnosed based upon perturbations from the models average pre-industrial climate state. But that is the only way it makes sense, because the warming in response to a perturbation (say, a doubling of atmospheric CO2) involves changes in (say) clouds from their average pre-industrial state. The fact that sunlight shining on a theoretical cold, dark earth creates warming which creates clouds (“climatologists forgot the sun is shining“) is not relevant to climate sensitivity — and even the climate models themselves (run from a cold, dark Earth state) will produce the process which Monckton imagines controls climate sensitivity.
I consider Christopher Monckton a friend, and I implore him to stop chasing this rabbit. I am asked about his ideas from time to time, and as a result I must, once again, attempt to explain why I believe he is wrong.
via Roy Spencer, PhD.
October 4, 2022 at 09:46AM