The widely reported global average surface temperatures, which are the basis for the global warming scare, are inaccurate, based on not on actual measurements but instead are the output of flawed complex statistical models.
Widely reported global average surface temperatures, which are the basis for the global warming scare, are inaccurate based not on actual measurements but rather the output of flawed complex statistical models.
Climate models are tuned to a supposed rapid surface warming that occurred in the two decades between roughly 1978 and 1997. Scientists speculate human causes to explain this warming then project dangerously high future temperatures from the same causes.
But the satellites show no such warming in the atmosphere over this period, strongly suggesting that the surface statistical models are wrong.
There are at least ten things wrong with these statistical models, including well-known flaws like arbitrary adjustments and the urban heat island effect. Other weaknesses include local heat contamination, the use of area averaging and interpolation, or the use of sea water proxies, as well as taking the mean value to be true when we know it is not. But most serious flaw is the surface statistical models are operating on what is called in statistics an “availability” or “convenience” sample.
While proponents of the theory humans are causing dangerous global warming claim to know the global surface temperature to a hundredth of a degree, a hundredth of a degree is incredible accuracy given that temperatures around the globe on many days can differ by a hundred degrees or more F and is not credible. These surface statistical models and the temperatures they estimate are worthless. They are based on probability theory, one of the absolute requirements of which is that samples used be random but the samples used in the surface statistical models are not random rather they are heavily clustered near urban areas and airports in developed countries.