All models are wrong, but models are not all equally wrong. Indeed, they can be wrong to different degrees and in entirely different ways. Here, we show that GCMs which are lower-performance (for particular tasks and applications) play a crucial role in climate science research. That is, lower-performance models help scientists gain knowledge they would otherwise lack, a point that is often underappreciated and has been under-theorized. More specifically, in the climate science literature, we see that lower-performance models help constrain the estimates of climate variables, lower-performance models provide data to test model weighting schemes, and lower-performance models serve as evidence to help resolve model-data discrepancies. This implies that (i) lower-performance models ought not be eliminated from analysis too hastily and (ii) the value of multi-model ensembles goes beyond exploring structural uncertainty and includes the counterintuitive generation of new knowledge via, in part, lower-performance models. As a result of (ii), model intercomparison efforts require reappraisal, particularly when deciding how to allocate modeling resources.

所有模型都是错误的，但模型的错误程度各不相同。事实上，他们可能会在不同程度上、以完全不同的方式犯错。在这里，我们展示了性能较低的 GCM（对于特定任务和应用）在气候科学研究中发挥着至关重要的作用。也就是说，较低性能的模型可以帮助科学家获得他们原本会缺乏的知识，这一点经常被低估并且理论化不足。更具体地说，在气候科学文献中，我们看到较低性能的模型有助于限制气候变量的估计，较低性能的模型提供数据来测试模型权重方案，而较低性能的模型则作为帮助解决模型数据问题的证据。差异。这意味着（i）不应过于匆忙地从分析中消除性能较低的模型，以及（ii）多模型集成的价值超出了探索结构不确定性的范围，还包括部分通过性能较低来生成违反直觉的新知识楷模。由于 (ii)，模型比对工作需要重新评估，特别是在决定如何分配建模资源时。