Calibrating an expensive computer model, such as a climate model, is an extremely challenging and involved problem, yet it is one that some would claim is, in principle, solved. Application of the Kennedy and O¡¯Hagan framework for probabilistic Bayesian calibration, allowing simultaneous tuning of input parameters and of systematic model discrepancy given a one-off space filling design in the input parameters and a set of observations of the real world, is ubiquitous and is, perhaps, considered ¡°off the shelf¡±. In this talk I will highlight some of the known dangers and drawbacks of taking this approach and argue for an alternative multi-wave approach to calibration known as history matching. The multi-wave approach raises a number of design questions and we will examine some of these in the context of calibrating the NEMO ocean model.