Graphical representations of modelling processes play an important role in communicating the nature of models to their users, and to the downstream users of the outputs of those models. This paper takes an innovative approach to developing and disseminating graphical model representations in a consistent manner across a whole research community. By publishing these together as a journal paper, the rigour and accuracy of these representations has been subjected to peer review. The quality of the diagrams themselves is high, and the process undertaken to produce them is an examplar of good practice in disseminating science.

This perspective paper examines in detail the concept of explicability in a climate model, whether conventional physics-based dynamical models, or those incorporating components based on machine learning. Everyone with an interest in climate models or their outputs would benefit from understanding the processes by which we can understand the importance and accuracy of these models and the methods by which it is possible to make sense of those outputs. This paper is a major contribution to that understanding. It is also very well written and should be widely read in the field.