In “Ice: The Ultimate Human Catastrophe”, Sir Fred Hoyle argued that the conditions for inducing an ice age may develop within a decade's time and outlines necessary precautions to avert this catastrophe
That book was published in 1981 and I remember a documentary at the same time, with Fred Hoyle against a snowy white backdrop.
I don’t think anyone, even climate change sceptics, would be inclined to give a huge amount of merit to Sir Fred’s arguments. But this is being used by sceptics as a means of arguing that scientists get climate change wrong. The argument goes: Fred Hoyle got it wrong then, along with many other scientists. Might today’s scientists have also got it wrong today?
But that is to overlook the fact that we have much better weather models than the 1980s, and satellites can provide us with vastly more data to work with.
In its simplest forms, air can be modelled as if it were a kind of fluid. Fluid dynamics is that branch of mathematics which deals with flow, whether of water or of currents of air. The equations which describe flow use what are called, in technical mathematical terms, partial differential equations. These are equations which do not have exact solutions, but can only be solved using numerical methods.
Put in layman’s terms, the equations governing weather patterns can be likened to a grid. It is a three dimensional grid, and you gather data from the points in your grid to predict the weather. Think of it in two dimensional terms, like placing points on graph paper. The shipping forecast, with its litany of weather stations, and weather conditions – wind speed, visibility, air pressure, gives us just a few points. The more points you have, the more accurate your picture of what is happening with the weather.
Back in 1981, computing power was very primitive, and so was data collection from points in the grid. Ground stations, weather radar, and even weather balloons contributed data to the model. Of course, now we have satellites which can add massive amounts of data to the picture, and computers which are also vastly faster than anything conceivable in 1981.
On the small scale, where it interests us in particular localities, weather forecasts can run for about 10 days, becoming increasingly inaccurate because of the chaotic nature of weather systems. That is the kind of model which can tell us what the weather will be like in Jersey, so the model is trying to fine tune to a specific location. We see this where weather forecasters give a picture of different weather across the British Isles. The larger the region, the more likely the weather is to fit the model, and because Jersey is such a small island, a passing shower might bypass the island altogether.
Larger scale climate models look at the bigger picture, the global picture. There include general circulation model (GCM). It employs a mathematical model of the general circulation of a planetary atmosphere or ocean. It uses the Navier–Stokes equations on a rotating sphere with thermodynamic terms for various energy sources (radiation, latent heat). These models are based on the integration of a variety of fluid dynamical, chemical and sometimes biological equations.
Again, the knowledge we have about weather patterns on a global scale is vastly better than that of Fred Hoyle. Part of that is because we have much better knowledge, through planetary probes, of weather patterns on other planets. A model that can accommodate more than one planet and improved accordingly is likely to be much better than a model which takes data only from one planet.
What these models try to do for the earth is to provide simulations that show “plausible" agreement with the measured temperature anomalies over the past 150 years, because for the most part, that is the period in which some kind of decent enough records have been kept.
t’s a bit like having points on a graph, and trying to find an mathematical equation that manages to pretty well go through all of them.
Climate researchers around the world use climate models to understand the climate system. Thousands of papers have been published about model-based studies. Part of this research is to improve the models. Some models provide a better fit than others, especially when dealing with the complexities of weather patterns. This is, after all, a scientific endeavour. But models which stand up to scrutiny and can be improved are giving us a much better picture all the time.
Cloud effects, in particular, are difficult to simulate. Cloud effects are still a significant area of uncertainty in climate models. They have competing effects on climate. They cool the surface by reflecting sunlight into space; but they warm it by increasing the amount of infrared radiation transmitted from the atmosphere to the surface.
Now the improving models and discarding of bad models is something we see elsewhere in science, and according to Karl Popper in “Conjectures and Refutations”, it is the way in which science advances, with better models for the physical world. It’s a feedback effect.
In relation to that, it should be noted that a count of tens of thousands of peer-reviewed scientific articles, shows that only about half of one percent suggested climate change was not occurring, and those are old articles.
In science, we cannot simply weigh up old and new, and put them in a set of scales. If that was the case, centuries of belief in the Ptolemaic model of the solar system could be brought in as a heavy counterweight to the Newtonian model. We can see clearly that is not how science works, so we have to look at the newer data, the newer models, all the time with both weather predictions and climate change models.
This has only been a brief look at climate and weather models, and there is enough information elsewhere about climate change for people to consider. But I hope it shows that while there are degrees of uncertainty, the models are improving, and that’s what we expect in science. It doesn’t mean the models cannot provide useful guidance. After all the models used for orbiting the moon and earth were not without some uncertainty, but we still managed to land a man on the moon using them.
For an excellent guide to the history of models of climate, past and present, and how they gathered evidence, and how views changed and developed from better evidence, I would refer the reader to this article, for which no advanced mathematics or indeed mathematics at all, is needed.