In model studies, a careful consideration of uncertainty is needed. Parameter and data uncertainty can be explored by sampling, but model structure uncertainty is more challenging to capture since the underlying hypotheses of many models are not directly clear. This study explores whether model structure can be inferred from model output. We created a dendrogram (family tree) based on model structure using a modular modelling framework. Subsequently, we created dendrograms based on model output. We determined the correlation between both dendrograms to analysed if model structure families could be inferred from model output. Results from this experiment over 671 climate instances showed that the performance of the inference depends on the type of output evaluated, and the climate. However, the performance of the inference is overall low, implying that model structure cannot be inferred from model output. These results demonstrate the need to further investigate opportunities to sample model space.

在模型研究中，需要仔细考虑不确定性。可以通过采样来探索参数和数据的不确定性，但是要捕获模型结构的不确定性更具挑战性，因为许多模型的基本假设尚不明确。本研究探讨了是否可以从模型输出中推断出模型结构。我们使用模块化建模框架基于模型结构创建了树状图（家谱）。随后，我们根据模型输出创建了树状图。我们确定了两个树状图之间的相关性，以分析是否可以从模型输出中推断出模型结构族。该实验对671个气候实例进行的实验结果表明，推断的效果取决于所评估的输出类型和气候。但是，推理的性能总体较低，暗示不能从模型输出中推断出模型结构。这些结果表明需要进一步研究对模型空间进行采样的机会。