So, how did they do this? They take proxy data (5 sites plus a multi-proxy for the Northern Hemisphere) and use spectral analysis to determine a set of sinusiodal variations that fit this proxy data. The output from this spectral analysis is then fed into an artificial neural network (a form of machine learning) which is then used to project the warming for the period 1880-2000 for the Northern Hemisphere and at the individual proxy sites. They find that the observed warming, since the mid-1800s, can mostly be explained as being a consequence of these natural fluctuations. The residual is then used to estimate the ECS, which they suggest is around 0.6oC.
Well, this is simply nonsense. It’s essentially just a complicated curve-fitting exercise. The average temperature of the Earth is largely constrained by energy balance. This, of course, does not mean that it can’t vary, but we do mostly understand what can cause these variations. There are internal/natural cycles that can produce variations, but there are limits as to how large these internally-driven cycles can be and how long they can last. On timescales much longer than a decade, or so, we would expect these to be small, otherwise it would indicate that our climate is much more sensitive to perturbations than we expect (exactly the opposite of what this paper suggests).
Long-term (multi-decade) changes in our climate are mostly a consequence of external perturbations; volcanoes, the Sun, emission of greenhouse gases, changes in ice sheets (typically a consequence of variations in our orbit). These are all rather complex processes and the idea that one could predict how they will change in future by fitting some sine curves to a few different temperature proxy records is rather ridiculous.
This highlights the key problem with the approach in this paper; you can’t try and understand what causes our climate to vary, or how it might vary in future, using machine learning alone. Even though our climate is complex, it is still a physical system and we do understand the underlying physical processes quite well. You do need to take this into account. The idea that (as the paper suggests)
[a]n alternative approach, as demonstrated here, does not require a prior understanding of the physical processes, but adequate data and appropriate machine learning techniques
is ridiculous. If you don’t consider the underlying physics, then you essentially know nothing about what’s causing the climate to change/vary.
That’s not to say that machine learning can’t play a role. However, if you are going to use something like machine learning to make predictions about the future, you do need to be pretty confident that the data that you use to train the machine learning algorithm presents a reasonable representation of the system you’re trying to model. This requires some actual understanding of the system being considered. If you change it by, for example, pumping lots of greenhouse gases into the atmosphere, then the training data will almost certainly not be appropriate.
Ultimately, if your naive approach – that completely ignores physics – produces a results that is inconsistent with our understanding of the physical system (suggesting, for example, that it’s almost all natural and that the ECS is about 0.6oC), then it’s much, much more likely that the machine learning algorithm is producing nonsense, than there being something wrong with what is essentially fairly basic physics.