By Lambert Strether of Corrente
Hamlet: Do you see yonder cloud that’s almost in shape of a camel?
Polonius: By th’ mass, and ’tis like a camel indeed.
Hamlet: Methinks it is like a weasel.
Polonius: It is backed like a weasel.
Hamlet: Or like a whale.
Polonius: Very like a whale. –William Shakespeare, Hamlet, Act 3 Scene 2
“Clouds were there for everyone—no tax as yet on them—free” –Alfred Stieglitz
I thought, in my perambulations through the biosphere, I’d move on from earth and water to the atmosphere, though why I settled on clouds, I don’t know (possibly this). Or perhaps I had aerosols on my mind. Typically, I begin with images showing the beauty of the subject, which in this case is not hard. From Alfred Stiglitz’s series, Equivalents, this:
Stiglitz was not the first to point his camera at the sky, but he seems to have been the first to make clouds his subject. He thought “his depictions of clouds represented his emotions,” but I say he was just projecting, like Hamlet, and the photographs are none the worse for that.
Be that as it may, clouds are almost infinitely various, which Stiglitz knew, and which the World Meteorological Organization (WMO) points out in its Cloud Atlas:
Clouds continuously evolve and appear in an infinite variety of forms. However, there is a limited number of characteristic forms frequently observed all over the world, into which clouds can be broadly grouped in a classification scheme. The scheme uses genera, species and varieties. This is similar to the systems used in the classification of plants or animals, and similarly uses Latin names.
To these characteristic forms we now turn.
Clouds, the Basics
For completeness, let’s define clouds. From the WMO:
A cloud is a hydrometeor* consisting of minute particles of liquid water or ice, or of both, suspended in the atmosphere and usually not touching the ground. It may also include larger particles of liquid water or ice, as well as non-aqueous liquid or solid particles such as those present in fumes, smoke or dust.
NOTE * “Hydrometeors consist of liquid or solid water particles. They may be suspended in the atmosphere, fall through the atmosphere, be blown by the wind from the Earth’s surface or be deposited on other objects. Snow or water on the ground is, by convention, not considered a hydrometeor.” “Meteor,” says my OED, is “Any atmospheric phenomenon. Now techn[ical]”
However, I must say I prefer NOAA’s:
A visible aggregate of minute water droplets or ice particles in the atmosphere above the Earth’s surface.
Because if clouds were not visible, Steiglitz could not have photographed them! And why are they visible? NASA:
A cloud is a mass of water drops or ice crystals suspended in the atmosphere. Clouds form when water condenses in the sky. The condensation lets us see the water vapor.
(We’ll get to condensation later.)
As WMO says, despite their infinite variabilty, clouds can be classified into genera, species and varieties. Of the genera, there are ten. Here is a chart (which spells out the cloud names, unlike the WMO equivalent):
Here is WMO’s logic diagram for classifiying clouds into genetra:
(So now, when you are out on your daily walk, you can look up at the sky and classify the clouds!)
Now, beyond genera, when you get into species, varieties, supplementary features, varieties, accessory clouds, mother-clouds, and special clouds, the complexity overwhelms; here is a table.
Picking up on NASA’s definition, let’s look at how clouds form.
How Clouds Form
Turning once again to NASA, “How Do Clouds Form?”:
Clouds form from water in the sky. The water may evaporate from the ground or move from other areas. Water vapor is always in the sky in some amount but is invisible. Clouds form when an area of air becomes cooler until the water vapor there condenses to liquid form. At that point, the air is said to be “saturated” with water vapor. The air where the cloud forms must be cool enough for the water vapor to condense. The water will condense around things like dust, ice or sea salt – all known as . The temperature, wind and other conditions where a cloud forms determine what type of cloud it will be.
More on condensation nuclei from the National Weather Service:
[W]ater molecules in the atmosphere are too small to bond together for the formation of cloud droplets. They need a “flatter” surface, an object with a radius of at least one micrometer (one millionth of a meter) on which they can form a bond. Those objects are called nuclei.
Nuclei are minute solid and liquid particles found in abundance. They consist of such things as smoke particles from fires or volcanoes, ocean spray or tiny specks of wind-blown soil. These nuclei are hygroscopic meaning they attract water molecules.
Called “cloud condensation nuclei”, these water-molecule-attracting particles are about 1/100th the size of a cloud droplet upon which water condenses.
Therefore, every cloud droplet has a speck of dirt, dust or salt crystal at its core. But, even with a condensation nuclei, the cloud droplet is essentially made up of pure water.
A cursory search on the sources of cloud condensation nuclei (CCN) yields research on sea algae, algae-killing viruses, phytoplankton, sea spray, bacteria, pollen, fires, ultrafine particles over the Amazon, dust from droughts, and human pollution tracks (e.g., plumes). I’m sure there are many, many more (one thinks of PM2.5 particles in large cities). Note the variety. From Nature:
Cloud condensation nuclei (CCN) provide the sites on which droplets form, resulting in clouds with radiative properties determined in part by CCN abundance and characteristics.
And from the Journal of Applied Meteorology:
Cloud condensation nuclei (CCN) at cloud base strongly affect the droplet concentration at cloud base, which in turn influences the life history of a cloud.
So, if you don’t understand CCNs, you don’t understand clouds. You can, of course, have proxies for understanding (parameters), and you can use those in models.
Clouds and Climate Modeling
From the American Meterological Society, way back in 2005, “Cloud Feedbacks in the Climate System: A Critical Review“:
The blueprint for progress must follow a more arduous path that requires a carefully orchestrated and systematic combination of model and observations. Models provide the tool for diagnosing processes and quantifying feedbacks while observations provide the essential test of the model’s credibility in representing these processes. …. Aspects of these parameterizations remain worrisome, containing levels of empiricism and assumptions that are hard to evaluate with current global observations. Clearly observationally based methods for evaluating cloud parameterizations are an important element in the road map to progress…. The blueprint for progress must follow a more arduous path that requires a carefully orchestrated and systematic combination of model and observations
I don’t have an objection to science progressing by putting the right foot (observation) in front of the left foot (modeling) over and over again, and so walking forward. I do have trouble with “trust the science” (or scientists) absent critical thinking, as a class-driven dogma.) What is “parameterization” and why is it a problem? From Nature, fifteen years later (!):
However, cloud data sets derived from multiple satellites over several decades suffer from spurious artefacts related to changes in satellite orbit, instrument calibration and other factors. These artefacts are particularly large when estimating globally averaged cloud cover, currently preventing any reliable estimation of trends in one direction or the other.
. But there is a problem. Clouds are on too small a scale to be represented using the laws of physics in current climate models. Instead, they are represented by . These do encode some basic ideas of cloud physics — clouds’ dependence on the ambient temperature, humidity and vertical air velocity, for example — but they are far from being ab initio estimates.
I would urge that if we are to combine a systems approach with observation, our models must incorporate cloud formation; that is, must incorporate CCN from — let me get the list — sea algae, algae-killing viruses, phytoplankton, sea spray, bacteria, pollen, fires, ultrafine particles over the Amazon, dust from droughts, and human pollution tracks. Among many others. This is difficult to do. From 2004, “Impact on modeled cloud characteristics due to simplified treatment of uniform cloud condensation nuclei during NEAQS 2004“:
Clouds are one of the most difficult physical phenomena for atmospheric models to reproduce…. Accurately reproducing the impact of clouds in atmospheric models requires a reasonable representation of CCN. … It is potentially possible to tune a fully prescribed CCN distribution to yield accurate longterm means, but day-to-day results will have errors that are likely to impact regional energy budgets and whose frequency could change in altered climate scenarios. This suggests that [Global Climate Models] which simulate vertical and temporal fluctuations in CCN distributions are likely to be much more accurate and better able to capture regional cloud variations.
So instead of working from CCN observation, we have worked from parameterization (“tune a fully prescribed CCN distribution”) instead. Even in 2020. From Geophysical Research Letters, “Anthropogenic Effects on Cloud Condensation Nuclei Distribution and Rain Initiation in East Asia:”
More CCN will lead the smaller the drops and the greater the liquid water content, the greater the cloud albedo, but their influence on cloud growth and on precipitation formation is still unclear.
And also from 2020, Atmospheric Chemistry and Physics, “Cloud condensation nuclei characteristics during the Indian summer monsoon over a rain-shadow region“:
[Atmospheric particles (APs)] which act as the cloud condensation nuclei (CCN) at a specific supersaturation (SS) can indirectly affect the climate by altering the cloud microphysical properties…. In the real atmosphere, the SS measurements are seldom possible, and the large disagreements between the CCN and cloud droplet number concentration remain elusive (Moore et al., 2013). All these effects eventually modify the precipitation pattern (Lohmann and Feichter, 2005; Rosenfeld et al., 2008). (IPCC, 2013).
All this said, a combination of tweaking the parameters and incorporating observational studies has finally reached a point where clouds, to mix a metaphor, have upset the climate science applecart. From Yale Environment 360, “Why Clouds Are the Key to New Troubling Projections on Warming“:
It is is the most worrying development in the science of climate change for a long time. An apparently settled conclusion about how sensitive the climate is to adding more greenhouse gases has been thrown into doubt by a series of new studies from the world’s top climate modeling groups.
Last month, American and British researchers, led by Zelinka, reported that 10 of 27 models they had surveyed now reckoned warming from doubling CO2 could exceed 4.5 degrees C, with some showing results up to 5.6 degrees. .
Zelinka said the new estimates of higher climate sensitivity were primarily due to changes made to how the models handled cloud dynamics…..
Modelers have also changed how they characterize the effect of anthropogenic aerosols from burning fuel, particularly in clouds.
Anthropogenic aerosols are CCNs. So cloud formation is key.
And the Guardian:
The role of clouds is one of the most uncertain areas in climate science because they are hard to measure and, depending on altitude, droplet temperature and other factors, can play either a warming or a cooling role. For decades, this has been the focus of fierce academic disputes.
Previous IPCC reports tended to assume that clouds would have a neutral impact because the warming and cooling feedbacks would cancel each other out. But in the past year and a half, a body of evidence has been growing showing that the net effect will be warming. This is based on .
“Finer resolution” means more accurate parameterization. Cloud formation is one aspect of “advanced cloud microphysics.” Scientific American has a reasonably balanced summary of the state of play:
Clouds are notoriously difficult to simulate in climate models. In the first place, cloud formation is a highly complex phenomenon with a lot of small, moving parts.
Tiny particles in the air [CCNs], called aerosols, have a huge influence on how quickly clouds form, how big they get, what type of clouds they turn out to be and how long they last in the atmosphere. That‘s on top of all the other weather-related factors that affect cloud formation, including air temperature, humidity and wind conditions.Simulating these complicated physics takes a lot of computing power and requires models to operate at a very fine scale. That‘s hard enough. But it‘s extraordinarily difficult to do in global climate models, which are designed to simulate grand-scale climate processes across the entire world.
To compromise, climate models often contain simplified, built-in information about clouds and the way they form—a kind of shortcut that allows clouds to appear without requiring the models to actually recreate all the small-scale physical processes that influence their formation.
Many of the new climate models have made significant advancements in the way they represent clouds and aerosols. They might better depict the amount of liquid water versus the amount of ice that certain clouds contain. Or they might more accurately represent the way certain kinds of aerosols influence cloud formation.
It‘s possible that in some cases these feedbacks are a little too strong. The clouds themselves might be more accurate, but the way they interact with the bigger climate system might still need to be tweaked. whether that‘s the case and how realistic the increased sensitivity really is.
Meanwhile, if your view of “science” is that it’s entirely based on observation — or that it does not involve debate — it will be problematic for you that our climate scientists have not been able to give a systemic account of cloud formation and its impact; we are still parameterizing, though it does seem we are parameterizing better. (Perhaps we should be putting quantum computers to work on this, when we have them up and running, instead of devoting all that power to, say, marketing or financial speculation).
Does this matter?
The Precautionary Principle
Nicholas Nassim Taleb would argue no. From Joseph Norman, Rupert Read, Yaneer Bar-Yam, and Nassim Nicholas Taleb, Climate models and precautionary measures, Issues in Science and Technology (Summer 2015):
The policy debate with respect to anthropogenic climate-change typically revolves around the accuracy of models. Those who contend that models make accurate predictions argue for specific policies to stem the foreseen damaging effects; those who doubt their accuracy cite a lack of reliable evidence of harm to warrant policy action.
These two alternatives are not exhaustive. One can sidestep the “skepticism” of those who question existing climate-models, by framing risk in the most straightforward possible terms, at the global scale. That is, we should ask “what would the correct policy be if we had no reliable models?”
We have only one planet. This fact radically constrains the kinds of risks that are appropriate to take at a large scale. Even a risk with a very low probability becomes unacceptable when it affects all of us – there is no reversing mistakes of that magnitude.
Without any precise models, we can still reason that polluting or altering our environment significantly could put us in uncharted territory, with no statistical track-record and potentially large consequences. It is at the core of both scientific decision making and ancestral wisdom to take seriously absence of evidence when the consequences of an action can be large. And it is standard textbook decision theory that a policy should depend at least as much on uncertainty concerning the adverse consequences as it does on the known effects.
I am with Taleb.
 No, not that Cloud Atlas, a movie I really enjoyed — ” I will not be subjected to criminal abuse” — when I saw it on a long-haul flight.
 Shout out to the National Weather Service for encouraging citizen science:
Citizen scientists wishing to report snow and rainfall amounts are encouraged to join the community collaborative rain, hail and snow network. Daily reports are sent via the internet and used by the National Weather Service. To become an observer, visit https://t.co/XPrqwcTYKl. pic.twitter.com/Q2nsdNmXie
— NWS GSP (@NWSGSP) December 11, 2020
 It may be, of course, that my Covid thoughts are bleeding into my cloud thoughts; I think a lot about particles floating in the air. However, several articles refer to CCNs, in the aggregate, as aerosols. So there you are. Of course, fluid dynamics are notoriously expensive, computationally….