 First, differences between modeling results and observational data might not be due to shortcomings of the climate model. Climate datasets can contain errors or have uncertainties that are underestimated or not reported. Indeed, in more than one case, it has turned out that model-data conflicts were resolved largely in favor of the models (see, e.g., Lloyd 2012). There also have been cases where a poor fit between modeling results and data was judged to be due in part to errors in assumptions about the external conditions (e.g., aerosol concentrations) that obtained during the simulated period (e.g., Santer et al. 2014, 2017). More fundamental is an issue related to the initial conditions of climate simulations (see also Schmidt &amp; Sherwood 2015). Oftentimes, a simulation for a particular period is launched from a representative state that is arrived at after letting the climate model run for a while under external conditions like those assumed to hold at the start of the period of interest; this “spin up” of the model is needed to allow its various components to come into balance with one another. Since this representative state inevitably differs somewhat from the actual conditions at the start of the period of interest (e.g., on Jan 1, 1900), and since the climate system is believed to be chaotic, the simulation cannot be expected to closely track observations throughout the period, even if the model perfectly represents all climate system processes; the goal is for differences between climate simulations and observations to be no larger than would result when running a perfect model from other initial states that are consistent with the external conditions at the start of the period of interest (i.e., no larger than differences stemming from internal variability).