Clouds From Both Sides Now
Guest Post by Willis Eschenbach
Clouds are said to be the largest uncertainty in climate models, and I can believe that. Their representation in the models is highly parameterized, each model uses different parameters as well as different values for the same parameters, and so of course, different models give very different results. Or to quote from the IPCC, the Intergovernmental Panel on Climate Change:
In many climate models, details in the representation of clouds can substantially affect the model estimates of cloud feedback and climate sensitivity. Moreover, the spread of climate sensitivity estimates among current models arises primarily from inter-model differences in cloud feedbacks. Therefore, cloud feedbacks remain the largest source of uncertainty in climate sensitivity estimates.
The question of importance is this—if the earth heats up, will clouds exacerbate the warming or will they act to reduce the warming? The general claim from mainstream climate scientists and the IPCC is that the clouds will increase the warming, viz:
All global models continue to produce a near-zero to moderately strong positive net cloud feedback.
My own theory is that clouds and other emergent climate phenomena generally act to oppose any increases in surface temperature. So me, I’d expect the opposite of what the models show. I figured that there should be a negative cloud feedback that opposes the warming.
So I thought I’d take a look at answering the question using the CERES satellite dataset. As a prologue, here’s a short exposition about measuring the effect of clouds.
Clouds have two effects on the surface radiation balance, and thus on the surface temperature. On the one hand, they reflect sunlight (shortwave radiation, “SW”) back out to space, cooling the surface. And on the other hand, clouds block and absorb upwelling thermal (longwave, “LW”) radiation from the surface, and they re-radiate about half of what is absorbed back down towards the surface. This additional downwelling radiation leaves the surface warmer than it would be in the absence of the clouds.
We can actually physically perceive both of these effects. During a clear summer day, a cloud comes over and instantly cools us down. And during a clear winter night, a cloud comes over and we immediately feel warmer.
These two changes, cooling and warming from different phenomena, are lumped together under the term “CRE”, which stands for the Cloud Radiative Effect. As mentioned above, it has a shortwave (SW) and a longwave (LW) component, and when added together these give us the “Net CRE”. Planetwide, as is generally known, the net CRE averages out to a surface cooling effect of about -20 watts per square metre (W/m2). That is to say, clouds cool the surface more than they warm it. Here’s how that plays out around the planet.
Figure 1. Net cloud radiative effect (LW warming minus SW cooling)
Note the strong cooling along the Inter-Tropical Convergence Zone (ITCZ) above the Equator, and in the Pacific Warm Pool north of Australia. There, the clouds are cooling things by up to sixty watts per square metre (W/m2). As a comparison, a doubling of CO2 is said to increase warming by 3.5 W/m2, an order of magnitude less …
And here’s the same image, but from the Atlantic side:
Figure 2. As in Figure 1, Atlantic side. Net cloud radiative effect (LW warming minus SW cooling)
As you can see, clouds have a net cooling effect everywhere except over some deserts and at the poles. At the poles, clouds actually warm the surface. And on average, the cooling is much greater over the oceans (-25 W/m2) than over the land (-8 W/m2).
In short, the clouds are cooling the hot tropics and warming the cold poles, just as my theory predicts.
The real question, however, is not the static condition. It’s what happens as the planet warms. For that, I calculated the changes in the net CRE with respect to surface temperature for each 1°C x 1°C gridcell. Here are those results, again seen from both the Pacific and the Atlantic sides.
Figure 3. Change in net cloud radiative effect (LW warming minus SW cooling) per one degree C of surface warming. Negative values indicate that there is greater cloud cooling with increasing surface temperature.
And here is the Atlantic view.
Figure 4. As in Figure 3, but an Atlantic view. Change in net cloud radiative effect (LW warming minus SW cooling) per one degree C of surface warming. Negative values indicate that there is greater cloud cooling with increasing surface temperature.
Now, this is a most interesting result. As predicted by my theory that clouds are a major part of the thermoregulatory system keeping the planet from overheating, we find that almost everywhere on earth, as surface temperature increases, cloud cooling also increases (negative values). This is true in both hemispheres, in the tropics, on land, on the ocean, and in both the Arctic and the Antarctic. Only in isolated patches of the ocean does cloud cooling decrease with increasing surface temperature.
I’m currently in the process of writing up my theory that emergent phenomena act to keep the surface temperature within narrow bounds, for submission to some as-yet-undecided scientific journal. This analysis is most definitely evidence in support of that theory, so I’m glad I did this particular piece of work. But man, I hate writing for the journals. I always feel like I need to give myself a lobotomy to write in the thick turgid long-paragraph style that they like. Plus with the small word limits and only a given number of graphics, I feel like I’m fighting with my hands tied.
Ah, well, it’s just another part of life’s rich pageant, and I learned an important lesson in my 17 years living on small South Pacific islands—the universe truly doesn’t give a shift what I want to happen next.
So I’ll just have to keep on keeping on …
Tonight we have rain here in a dry year, so life is good. I got my second vaccine shot two days ago. Other than a sore arm and one day of feeling like I was hastily assembled out of random spare parts, not much in the way of side-effects. People have asked me why I got the vaccine … I say everyone has to decide for themselves the balance between their known COVID risk and the unknown vaccine risk.
Me, I’m 74, and if I didn’t do myself serious genetic damage in the ’60s and ’70s, it certainly wasn’t for lack of trying. Add in the odd co-morbidity or two, not unusual in a man of my late youth. Then there’s the fact that my gorgeous ex-fiancee is a front-line health worker, a family nurse practitioner who is exposed because she administers COVID vaccine shots and gives sports physicals at the local college. (And, I might add, she also did the same before she got vaccinated last month. Big props to her, and to all of the worlds’ medical personnel putting their lives on the line to fight the pandemic.)
Finally, and to our immense delight, our un-vaccinated daughter, son-in-law, and 19-month-old granddaughter are now all living together with us in our big old rambling house in the forest that I built with my own hands …
So getting the vaccine was an easy choice for me … but I don’t fault anyone for whatever they might choose.
Best regards to all, stay healthy,
As Usual: When you comment, I ask that you quote the exact words you are discussing, so we can all be clear as to both what and who you are responding to.
Technical Notes: I’m using the “surf_cre_net_tot” (surface CRE net total) file from the CERES EBAF (Energy Balanced And Filled) dataset for the CRE data. For the surface temperature, I’ve converted the “surf_lw_up_all” (surface longwave up all conditions) CERES file to temperatures using the Stefan-Boltzmann equation. This gives surface temperatures that are slightly different from the Berkeley Earth gridded surface temperature dataset … which in turn is slightly different from the HadCRUT gridded surface temperature dataset … which in turn is slightly different from the GISS LOTI gridded surface temperature dataset … they’re all four close, but which one is right? Nobody knows, so I use the CERES data. It has the huge advantage of agreeing in every gridcell with the energy flows given in the other CERES datasets, including of course the surf_cre_net_tot dataset I used in this analysis.
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