For simpler models you can do a sensitivity analysis by rerunning the model while systematically varying the uncertain parameters. Based on reading a handful of IPCC papers, a similar method is used in climate science for the most important parameters, but is severely limited by the resources needed to rerun the models and the sheer number of parameters.
Basically, any uncertainty due to complexity is mostly ignored and any skeptics who point this out are tarred as "deniers." Combine this with the fact that generally climate scientists do not produce any point predictions to verify models, and I think you can safely ignore many climate models. The papers that do produce explicit measurable predictions tend to find an interesting result by stretching the models to fit a narrative, eg: https://www.nature.com/articles/s41477-018-0263-1
Basically, any uncertainty due to complexity is mostly ignored and any skeptics who point this out are tarred as "deniers." Combine this with the fact that generally climate scientists do not produce any point predictions to verify models, and I think you can safely ignore many climate models. The papers that do produce explicit measurable predictions tend to find an interesting result by stretching the models to fit a narrative, eg: https://www.nature.com/articles/s41477-018-0263-1