Global warming and clouds.
Before talking about global warming, one thing must be clear. Nobody knows what it is. Nobody knows because it is such a multidisciplinary subject that it takes around thirty specialists to compose it. This means that no one, as a single individual, on the planet really understands global warming. What is done is to measure the scientific consensus of specialists.
If marine biologists, geologists, paleologists, solar astrophysicists, upper atmosphere physicists, lower atmosphere physicists, solar proxies experts, satellite survey experts, terrestrial biologists, cloud expert meteorologists, mathematical model experts, physics experts of gases, and so on, agree on something – in the sense that their papers make predictions that come true – we can say that there is "scientific consensus" on something.
It's not a parliament of science where you vote by majority, let's be clear: when I talk about scientific consensus I'm talking about publications with data, which are then subjected to peer review and cited.
When I hear someone say that they have "read the science behind global warming, I already know I'm dealing with a fool, because it is so multidisciplinary that no single human being can claim to know "the science behind global warming. warming”, unless you are literally omniscient. We can agree or not on the conclusions, i.e. an extremely synthetic work, but if we wanted to discuss the details, we would need about twenty sector experts, or a hundred "sub-specialists" in the niches of each sector. So "scientific consensus" is measured: not, I repeat, the quantity of scientists who agree – it is not a parliament – but the number of publications with convergent data.
“Antarctica is melting” is a simple statement to understand, but if you try to read the science behind it you need an Antarctic climate expert. And good too.
Having said that, essentially I only understand the part of climate warming that refers to work I have done, that is, big data and simulation. I'm an expert in this part, so I can read papers and understand what's on them. I say understand, because peer reviewing 350 exabytes of HDF data ( https://www.earthdata.nasa.gov/esdis/esco/standards-and-practices/hdf-eos5 ) is beyond computing power and storage I have. But at least I understand what they do.
Can I criticize the techniques? In principle yes in the sense that I would stop using techniques that are so sensitive to initial conditions, for example. And I would also stop pre-testing models with Pleistocene climate. And other things.
So I can read a little niche of what they do. And I write this because recently a funny German physics communicator made a rather alarming video.
So, let's try to explain the history of mathematical models (invariably built on computers): a famous black physicist joked that the science of global warming could make global warming worse due to the excessive use of computer simulations, and he might be right at least in part) in a simple way.
Lots of them are made. Every time we find a way to insert a new dataset (perhaps because we send a probe close to the sun and have better data on the energy emitted), hundreds, if not thousands, of new simulations are actually started.
Of these, very few survive. Some are canceled immediately because they foresee Sharknado-style scenarios (hurricanes so strong that they suck up salt water and make it rain on cultivated areas: if this happened, a similar force could also suck in cetaceans and boats as large as an oil tanker), others are canceled over time because predictions fail, but in a less striking way.
Once upon a time, there were models that envisaged global extinction scenarios of the human race, which were quashed, but they had political effects, such as creating movements such as the "Last Generation" or "Extinction Rebellion". There is, therefore, a proven phenomenon of "eco-anxiety": entire political movements born from the appearance of very catastrophic models. But I repeat, these models have been cancelled.
What they do is take a model and first see if it can “predict” “historical” data. In the sense that I put all the data I have up to 10,000 years ago, and see if I can "predict" something that we already know, like the climate of 9900 years ago.
(this is the part that I honestly criticize, because in my opinion this generates too much bias)
But let's move on. How are we doing? Today, around twenty mathematical models survive and are used in simulations and updated when new datasets appear. (new satellites, etc.).
You would think that if these models survive, then they are somehow saying the same thing. Let them agree.
No.
There are so-called "hot" models, which were built (i.e. tested) mainly on historical data, taken from periods in which the temperature increased). The data comes mainly from fossil trees whose rings are observed, perennial ice cores, and chemical geology, from which chemical compositions and temperatures can be extracted.
But these models were developed ONLY using data from historical periods where temperatures increased. And the interesting thing is that if we use a "hot" model, we get a "hot" forecast, that is, a forecast where the temperature of the planet increases.
But there are also models that have been tested ONLY using data from historical periods in which CO2 increased but temperature decreased. These models continue to predict the arrival of new ice ages. If you hear scientists say "whatever warming, an ice age is coming here" it's not because they are stupid: it's because they don't work on "hot" mathematical models, but rather on "cold" ones.
I imagine the questions you are asking yourselves. Are the models more "hot" or "cold"? There are more Hot ones.
But what does it mean? It simply means that it is easier for academics to get funding to create "hot" models. The funding mechanism for academic research is not rational but rather political (in Europe) or necessarily economic (private funding) in the USA. Russia and China, for example, prefer models of interest. So the Russians finance many "cold" models because they are afraid that a sudden drop in temperatures, even if only in winter, will kill several – very large – areas of their country. You understand that if you have areas that are at -43 during the winter, and they are areas from which you extract hydrocarbons (i.e. you need them for export), the idea that they drop to -60 worries you. Living for three months at -40 is difficult but not impossible, -60 for three months in a row becomes impossible. You will therefore hear Russian scientists fearful of the possible arrival of a strange glaciation.
And if we developed similar models in the West, we would probably have this fear. Since the “hot” models are successful in the Western industry, it is preferred to invest in those. It's easier to obtain funds, in short.
So the question about the difference in number between the "hot" and "non-hot" models is fair, but naive.
Another question: are you talking about artificial intelligence?
No. Basically what they do is not “train an AI with cold data”, or “train an AI with hot data”. The Bias here is predictable.
What they do is write models, usually based on finite differences (for this choice I criticize both the difficulty of meshing and the sensitivity to initial values), and then they begin to "test" them. They take blocks of historical data and see how they perform. If the computer simulation results match past data well, they consider it credible.
The fact that all models based on "hot" data predict warming and all those based on "cold" data predict cooling is a bizarre fact which in my opinion comes precisely, as I wrote, from the sensitivity of these models to initial conditions .
Why are there "hot" ones and "cold" ones? Why not throw everything into the cauldron?
Because in such a complex model what will happen is that the results will fluctuate. Even predicting ice ages is practically impossible: geologically speaking, it is a phenomenon that is not really as regular as we think. ( https://it.wikipedia.org/wiki/Cronologia_delle_glaciazioni ). And if we emerge from the Pleistocene, everything changes. But even the Pleistocene is not so regular.
The model above seems to relate CO2 to the Pleistocene ice ages, successfully (mostly), but if we use this:
We could clearly correlate climate with the isotope 18 of oxygen. Moral: never mix the indicators of a phenomenon with the cause of the phenomenon.
Furthermore, almost no one can say they understand what is happening, and even the famous MIlankovitch cycles explain the right thing, but definitely not everything. ( https://en.wikipedia.org/wiki/Milankovitch_cycles )
They therefore preferred to "break" the problem into smaller parts. But at this point you will wonder what sense it makes to use as tests climate models that also come from more ancient periods, when the ice ages did not yet exist. Interesting question, which clashes with Milankowitch cycles.
After this long explanation, let's get to the point.
Sabine raised an alarm, as a communicator, which sounds like this. “It all depends on a certain “climate sensitivity”, that is, a scalar value that represents the relationship between the increase in CO2 and global warming.
Statements of this kind leave me very perplexed. If you represent a phenomenon using a scalar to represent the growth of the phenomenon, you are telling me about a linear phenomenon. Seriously? But no, because this parameter is then used within a simulation that is not linear at all.
So saying “I'm worried about how tall he is” is a little hard for me. It's like taking a complex phenomenon (let's take one at random: the female orgasm) and making it depend on the size of the penis. We will deduce that the best possible partner for a woman is a sperm whale: two meters and eighty meters tall.
However, the thing is not linear, and the average woman begins to have problems beyond 18 centimeters, while porn stars reach much higher, but we don't know how many orgasms are real. (we honestly don't even know if female sperm whales have orgasms).
Moral: in a complex, nonlinear model, the size of a single parameter does NOT matter.
So no, I don't share the alarm: if I raise the climate sensitivity in a "hot" model I get an even warmer forecast, but if I put it in a model that predicts ice ages, I get even colder.
Why this error? Why reduce everything to a single parameter which is then multiplied with another parameter, i.e. the quantity of CO2 or greenhouse gases? Why does it seem sensible to look at ONE number and think that everything depends on that? (in turn, that number depends a lot on what the clouds do when the water is in a particular state, let's say "liquid that is not usually found in the air").
The problem is that we have always heard global warming explained as a phenomenon that depends only on greenhouse gases, and we have always heard it explained with an analogy, which was the greenhouse effect.
I know it may seem strange to you, but the greenhouse effect has NOTHING to do with global warming. It's an analogy that makes sense to use for popular purposes, but not for scientific purposes.
The greenhouse effect, first of all, works in a range that reaches just over twenty meters. Just make a greenhouse 50 meters high, and you will discover that you have built a kind of Stirling machine that is even colder at the base because it is even hotter at the top. Your plants will freeze even more.
It has nothing to do with it.
The real phenomenon has to do with the change in the behavior of the upper atmospheric layers. The Earth's temperature, in a spannometric way, can be described as the difference between the incoming energy and the radiated energy.
It is mainly the upper layers that radiate energy. If they are dense they contain a lot of energy, and therefore radiate a lot of it. This cools the planet. If the presence of CO2 makes some layers less radiant, or heavier, therefore less high, then all the energy is emitted from less dense and higher layers. But if they are less dense they will contain less energy and radiate less. As a result, the earth warms up.
It is therefore a modification that has little to do with the mechanism of the greenhouse effect. There is a whole interaction between atmospheric proxies involved, which requires different specialists. This is why the alarms about water as a greenhouse gas, or about methane, are poorly founded. They are greenhouse gases if you put them in a greenhouse, but in atmospheric terms their role is still uncertain.
But the fact remains that explaining global warming using the greenhouse effect was a mistake. And not because it is terribly wrong (it remains a simple analogy to use to explain), but because it puts people in the position of thinking that everything depends on a single factor (CO2, plus other gases), while to make a model decent, at least twenty specialists are needed, if not a hundred subspecialists.
Using an example for information purposes that seems to make everything depend on CO2 was, in my opinion, the most serious mistake. Because he made people believe that everything depends on ONE factor. Instead, the human presence has effects in multiple dimensions, and for this reason it is stupid to tell it like this.
Depicting the problem as something that depends on CO2, and only on fossil fuels, is a very serious mistake. It makes us believe that if we use electric cars powered by solar energy, everything will be fine. Instead we could discover that concreting weighs much more, because concrete alone and the CO2 it emits during concreting is the third factor in the production of CO2. And we're not doing anything. And what about volcanic phenomena that produce CO2?
But if we all go hunting for THE factor that triggers EVERYTHING, as Sabine does in fearing the growth of sensitivity, we are much further from the solution. Sure, maybe we have a more concise representation of the problem for the masses, but we are MUCH further from finding the solution.
Of course, CO2 is one of the most correlated factors, but I wouldn't be so sure that it is THE cause in a single sense, and I wouldn't even be sure that going back to 300 ppm would work. Modifying cars, for example, in my opinion it won't have any effect. Have we noticed significant drops in emissions with the spread of electric cars? Um… no.
The problem, that is, is that CO2 is an indicator of industrial activity as a whole. Consequently, saying that it is CO2 that causes the greenhouse effect makes sense, but it is like saying that we have a problem with industrial activity. For example, if we had used the value of stock market indices, we would have obtained the same result because industrial activity impacts BOTH CO2 levels and stock market indices in a very similar way. We could calculate the correlations, instead of using CO2, using a measure of industrial activity, and discover that the problem is industrial activity as a whole, and it is useless to focus on cars, because perhaps the climate transformation produces such a large increase in industrial activity that the savings obtained do not count.
In a nutshell:
- If we consider CO2 as the main chemical-physical cause, it may seem sensible to say that by switching to solar panels and reducing fossil fuels the CO2 situation will improve.
- If we consider that CO2 is an indicator of industrial activity, industrial transformation could increase CO2 in the atmosphere so much as to nullify any effort.
What does it mean?
It means that lowering CO2 must ALSO require the MINIMUM industrial transformation possible. Because CO2 is an indicator of industrial activity, and if the “green” transformation causes a huge increase in industrial activity, it may not be “green” for the planet at all.
I contest the idea that a simulation that takes into account hundreds of factors ends up indicating only one factor as the cause, only one multiplier (climate sensitivity) and only one solution, which does not take anything else into account.
This way of thinking is not part of the solution: as I see it, it is part of the problem.