 Construction of climate models also inevitably involves some tuning, or calibration, which involves the (often ad hoc) adjustment of parameter values or other elements of the model in order to improve model performance, usually measured by fit with observations. The observations that are targeted vary from case to case; they might relate to individual processes or to aggregate, system-level variables, such as global mean surface temperature (see Mauritsen et al. 2012; Hourdin et al. 2017). Which parameter values are adjusted depends on the observations with which a better fit is sought, but often adjustments are made within parameterizations, to parameters whose best values are significantly uncertain. The extent to which a model is tuned is usually not documented or reported in detail (but see Mauritsen et al. 2012 for an instructive example). Recently, there have been calls for more transparency in reporting tuning strategies and targets (Hourdin et al. 2017), in part because information about tuning is relevant to model evaluation (see  Section 4.4).  An interesting question, however, is exactly what counts as tuning since, even when formal quantitative comparison with a set of observational data is not performed, modelers can be familiar with those data and may well make choices in model development—choices which could reasonably have been somewhat different—with the expectation that they will improve the model’s performance with respect to those already-seen data.