Here is a fascinating article from a blog I haven't come across before called "A Chemist in Langley":
...the vast majority of the warmist community have a worldview that stresses Type I [false positive] error avoidance while most skeptics work in a community that stresses Type II [false negative] error avoidance. Skeptics look at the global climate models and note that the models have a real difficulty in making accurate predictions. To explain, global climate models are complex computer programs filled with calculations based on science's best understanding of climate processes (geochemistry, global circulation patterns etc) with best guesses used to address holes in the knowledge base. The models are "trained" by looking at historical data and seeing whether they can replicate what has occurred in the past. In a simplistic description they are trained to interpolate data and once they get good enough at interpolating data they are then used to extrapolate future conditions. Since the global climate models are works in progress they still do not do a great job at extrapolating, yet. In particular, these models have failed to predict the "pause" in the surface temperature data that has lasted for (depending with whom you talk) somewhere around 15 - 18 years. Essentially the model predictions and the measured temperatures have diverged.
Skeptics see the poor extrapolations and suggest a need to refine the models to address the divergence. From a Type II error avoidance viewpoint, given the relatively poor quality of the model predictions, putting limited resources into addressing potentially faulty predictions seems like a poor choice. Instead, resources should be allocated to improving the models and any additional monies spent on other "demonstrably real" problems out in the world. Warmists, on the other hand, point out that the models are the best tools we have to date and to ignore their predictions, just because they are imperfect, is a big mistake. Warmists point out there is a real risk that a lack of action now could result in a low-probability, high-cost outcome (a fat tail on the uncertainty distribution of the outcomes). So monies should be spent on avoidance immediately while we continue to refine the models.
(The link came to me by a circuitous route, but I believe the hat-tip is for Richard Tol)