The widely reported global average surface temperatures, which are the basis for the global warming scare, are not true. Contrary to popular belief these are not measurements. They are the output of complex statistical models. These statistical models are every bit as questionable as the climate models they feed into, actually more so.
The global warming scare is based on the supposed rapid surface warming that occurred in the two decades between roughly 1978 and 1997. The climate models are tuned to this warming, using speculative human causes to explain it. They then project this surface warming to dangerous future levels and that is the scare.
But the satellites show no such warming in the atmosphere over this period, where it should be if it were caused by greenhouse gases. The satellites show no warming at all over this crucial time. This zero warming strongly suggests that the surface statistical models are wrong.
Keep in mind that these global temperature statistics are no different than a voter poll prior to an election and we know how wrong they can be. An incredibly tiny subset of the overall population is being sampled. In this case the overall population is the temperature every place on earth at every moment over an entire year.
The pollsters know that a lot can go wrong. Apparently the alarmists that cite these crude temperature estimates as precise facts do not, or they choose to ignore the problems, in which case they are faking it.
There are at least ten things wrong with these statistical models. These flaws support the view that these crude temperature estimates are simply wrong. Some flaws are well known, like arbitrary adjustments and the urban heat island effect. Other weaknesses are less well known, like 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. These will be topics of later analyses.
But here I present the deepest flaw, which is not widely discussed. This is that the surface statistical models are operating on what is called in statistics an “availability” or “convenience” sample.
To begin with, note that the alarmists claim to know the global surface temperature to a hundredth of a degree. Here is an example from NOAA’s recent “Global Climate Report” for 2016:
“The average global temperature across land and ocean surface areas for 2016 was 0.94°C (1.69°F) above the 20th century average of 13.9°C (57.0°F), surpassing the previous record warmth of 2015 by 0.04°C (0.07°F).”
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. In fact it is not credible. The truth is that these surface statistical models are not merely inaccurate, they are worthless. Here is why.
The math of statistics is based on probability theory. Thus one of the absolute requirements is that the sample be random. If the sample is not random then the math is not applicable.
In fact the samples used in the surface statistical models are nothing like a random sample of the Earth’s surface. They are heavily clustered near urban areas and airports in developed countries. The locations were not chosen to be a global temperature sampling system and they certainly are not. The oceans are even worse because there are no fixed stations. Most of the Earth had no fixed temperature recording stations during the period in question, and still have none. There is no random sample of the Earth’s surface temperature.
In short the surface statistical models use the data that is available, not a random sample of the population. Statistical sampling theory makes clear that convenience samples like this cannot be used to estimate the statistics for the whole population. Yet this is exactly what is being done for global average temperature, to a hundredth of a degree. This is simply wrong.
Statistical science is very clear that a convenience sample does not provide an accurate estimate. Here are some examples from several statistical science websites:
A. “Research Methodology” says this:
“Disadvantages of Convenience Sampling:
Highly vulnerable to selection bias and influences beyond the control of the researcher
High level of sampling error
Studies that use convenience sampling have little credibility due to reasons above”
B. “ThoughtCo.com” says this:
“Problems with Convenience Samples:
As indicated by their name, convenience samples are definitely easy to obtain. There is virtually no difficulty in selecting members of the population for a convenience sample.
However, there is a price to pay for this lack of effort: convenience samples are virtually worthless in statistics.”
C. “Conveniencesampling.net” says this:
“Because of the flaws found in this form of sampling, scientists cannot draw concrete conclusions from their data.”
So the global warming scare is based on global statistics that have little credibility, are virtually worthless and cannot be used to draw concrete conclusions. What a mess!
Alarmist climate science is falling all over itself trying to explain a two decade period of rapid warming that the satellites say does not exist. They are faking it. There is nothing to explain.