Climate is an emergent system with many interacting processes and components. Complexity is essential to accurately model the system and make quantitative predictions. But this complexity obscures the different compensating errors inherent in climate models. The Anna Karenina principle, which assumes that these compensating errors are random, is introduced. By using models with different formulations for small-scale processes to make predictions and then averaging them, we can expect to cancel out the random errors. This multimodel averaging can increase the skill of climate predictions, provided the models are sufficiently diverse. Climate models tend to borrow formulations from each other, which can lead to “herd mentality” and reduce model diversity. The need to preserve the diversity of models works against the need for replicability of results from those models. A compromise between these two conflicting goals becomes essential.