As it turns out, the models they use are very unreliable, even on their own terms.Geographically distributed predictions of future climate, obtained through climate models, are widely used in hydrology and many other disciplines, typically without assessing their reliability. Here we compare the output of various models to temperature and precipitation observations from eight stations with long (over 100 years) records from around the globe. The results show that models perform poorly, even at a climatic (30-year) scale. Thus local model projections cannot be credible, whereas a common argument that models can perform better at larger spatial scales is unsupported.
In other words, while large computer models might be interesting as exercises in extended logic, they are not useful in the "real world" where we don't understand what we are modelling all that well. It's similar to a finding in another discipline taking advantage of math as a way to extend logic: papers that use "lemmas" rarely establish anything worth citing.
Models of argument that rely primarily on logic have been consistently rejected by audiences: everyone is aware that logic is a cute way to organize a presentation, but no guarantee that the conclusion is worth listening to.