This paper concludes the exact opposite and they can make their case in two paragraphs and one plot.
The study found that scientists come to opposite conclusions about the causes of recent climate change depending on which datasets they consider. For instance, in the graphs above, the panels on the left lead to the conclusion that global temperature changes since the mid-19th century have been mostly due to human-caused emissions, especially carbon dioxide (CO2), i.e., the conclusion reached by the UN IPCC reports. In contrast, the panels on the right lead to the exact opposite conclusion, i.e., that the global temperature changes since the mid-19th century have been mostly due to natural cycles, chiefly long-term changes in the energy emitted by the Sun.
Both sets of panels are based on published scientific data, but each uses different datasets and assumptions. On the left, it is assumed that the available temperature records are unaffected by the urban heat island problem, and so all stations are used, whether urban or rural. On the right, only rural stations are used. Meanwhile, on the left, solar output is modeled using the low variability dataset that has been chosen for the IPCC’s upcoming (in 2021/2022) 6th Assessment Reports. This implies zero contribution from natural factors to the long-term warming. On the right, solar output is modeled using a high variability dataset used by the team in charge of NASA’s ACRIM sun-monitoring satellites. This implies that most, if not all, of the long-term temperature changes are due to natural factors.
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