I examine error diagnosis (model-model disagreement) in climate model intercomparisons including its difficulties, fruitful examples, and prospects for streamlining error diagnosis. I suggest that features of climate model intercomparisons pose a more significant challenge for error diagnosis than do features of individual model construction and complexity. Such features of intercomparisons include, e.g., the number of models involved, how models from different institutions interrelate, and what scientists know about each model. By considering numerous examples in the climate modeling literature, I distill general strategies (e.g., employing physical reasoning and using dimension reduction techniques) used to diagnose model error. Based on these examples, I argue that an error repertoire could be beneficial for improving error diagnosis in climate modeling, although constructing one faces several difficulties. Finally, I suggest that the practice of error diagnosis demonstrates that scientists have a tacit-yet-working understanding of their models which has been under-appreciated by some philosophers.

我检查了气候模型比对中的错误诊断（模型-模型不一致），包括其困难、富有成效的示例以及简化错误诊断的前景。我建议气候模型相互比较的特征对错误诊断构成比单个模型构建和复杂性特征更大的挑战。比对的这些特征包括，例如，涉及的模型数量、来自不同机构的模型如何相互关联，以及科学家对每个模型了解多少。通过考虑气候建模文献中的大量示例，我提炼出用于诊断模型错误的一般策略（例如，采用物理推理和使用降维技术）。基于这些例子，我认为错误库可能有助于改进气候建模中的错误诊断，尽管构建错误库面临一些困难。最后，我建议错误诊断的实践表明，科学家对他们的模型有一种默契但有效的理解，而这种理解却被一些哲学家低估了。