Climate Sophistry 2: The Fallacy of the Failed Predictions Argument.
The double standard, the logical fallacy of the failed predictions argument.
One of the most common talking points of climate change deniers is that the predictions made by climate scientists haven’t happened as predicted and, therefore, the models are false and the science behind them is bad. The logical fallacy underlying this argument is quite common: The double standard. They are putting the climate models to a higher standard than the scientists who make them. It’s equivalent to saying that meteorology is bad because an extended weather forecast doesn’t happen as predicted. The scientists who make these models will be the first to state that they are approximations that model extremely complex systems that give possibilities and probabilities of something occurring, not certainties. In modeling the overall warming trend correlated with CO2 levels in the atmosphere, they have been generally accurate.
One well known climate model was produced by James Hanson and his colleagues at NASA in 1988 and gave three different possible scenarios and the second of the three has predicted 2016 level of CO2 to within 3ppm and global temperatures to around 10% of measured levels. Climate change deniers have been known to refer to the one of the other two possibilities in this study and omit the one that was accurate in their propaganda. This is another of their common techniques: Picking and choosing their facts and data. Real science doesn’t allow this, obviously. Climate change models are constantly being refined as new data is applied to them and the scientists involved will be the first to admit that they are a work in progress and not the final word.
In their use of the double standard, climate change deniers will generally pick the worst case scenario in a climate model, most often from a simplified, exaggerated and sensationalized media report that was designed to attract readers and viewers, not report accurately on the science in all its detail and complexity.
Reference links for further reading.