Basic propaganda technique, repetition, loses its strength if sheeple gets bored. You need to invents something new.
”Climate change” is a powerful meme, because you omit indefensible words in CAGW. You assume catastrophe; You assume CO2 as a cause; You assume warming, when you talk.
People start to realize that 0,5 C change is not significant. It is just noise in observations. Not worth of trillions of dollars of pain.
Cancer is something very small but dangerous, because it is living and grows. Connecting word ”cancer” to something is an example of a image marketing technique: transfer. Fears of chemicals and radiation have been very successful. No one have died, but industries have been destroyed. Expect attack on coal and diesel.
Propagandists need something emotional and something that can’t be easily proven wrong. You know, just in case, if ..
Popular myth of science is that you can have a proof of something. You can in mathematics and logic, but not in natural science. In climate science you can spot logical inconsistencies especially in MSM, where climate change results in warming and cooling at the same time.
Studies in natural science start with data. You slice and dice it and form a conjecture. That’s right. Correlation is not a proof, but it helps you to find a useful hypothesis. For example: CO2 emissions results in substantial global warming.
Then you establish tests that challenge your hypothesis.
– CO2 emissions increase the amount of CO2 in the atmosphere
– increased CO2 in atmosphere results in increase of surface temperatures
– increased surface temperatures result in more water vapor in the atmosphere
– increased water vapor in the atmosphere results in more increase in surface temperatures
– increased sea water temperatures result in sea level rise
– and so on
Previous examples should, of course, be more specific, more detailed. Each of them might be a research question in a peer-reviewed paper.
If any of these tests fail, the hypothesis must be changed. It might be true in some circumstances, but not in all of them. This is difficult because observations might also be wrong. In climate science bad data is a problem that may result in a conclusion that we don’t know.
Computer models are a special case of a hypothesis. Putting all your equations together could reveal inconsistencies in your thinking. If model’s behavior does not match the observations you may learn how to change the hypothesis.
Global Cooling on WUWT