Saturday, September 16, 2017


Climate Models Can’t Even Approximate Reality Because Atmospheric Structure and Movements are Virtually Unknown

Guest Opinion: Dr. Tim Ball
... Models vary from hardware models or simple scaled down versions of reality to complete abstractions. A model car is an example of the former and a mathematical formula with symbols replacing variables of the latter. The problem with the hardware is it is impossible to scale down many things because the physical properties change. For example, it is impossible to scale down the change of ice from solid and rigid to plastic and flowing as occurs in an alpine glacier in a hardware model. In the abstract model, each variable loses most of its real-world properties.

Climate models are abstract models, except they are made up of a multiple of models all interacting with each other. Those interactions bear little resemblance to reality.
In summary,
  • We have virtually no data.
  • This is true even for fundamental variables like temperature, precipitation atmospheric pressure, and atmospheric water content.
  • Data is replaced by symbols that eliminate most of the properties of the natural variable.
  • In many cases the “data” is generated in another model and used as ‘real’ data in the larger model.
  • The models are essentially static representations of average conditions. The one thing we know with certainty is that the Earth’s atmospheric system is dynamic, changing daily, seasonally and constantly over the course of time.
  • The models consistently fail the standard test of scientific understanding and accuracy by producing inaccurate predictions.
Initially, I learned the basics of weather and especially forecasting necessary for aviation. These were expanded when I gave lectures on aviation weather as an operations officer on an anti-submarine squadron flying over the North Atlantic and then for a search and rescue squadron flying in northern and Arctic Canada. I recall one search out of Fort Chipewyan, northern Alberta when we observed first-hand the severe limitations of knowledge and therefore forecast skills.