Evaluating the model performance is one of the most important tasks for developing and tuning a new model. Choosing among the various existing, different metrics and diagnostics for climate model evaluation is a challenge. The Accelerated Climate Modeling for Energy (ACME) team opted to use a set of necessary (but not sufficient) metrics and diagnostics as its priority, which includes basic dynamic, thermodynamic, radiation, cloud radiative effect, and precipitation variables. The Program For Climate Model Diagnosis and Intercomparison (PCMDI) metrics package (PMP) and a diagnostic tool have been used to examine well-established statistics (e.g., mean bias, uncentered root-mean-square error, correlation coefficient, and standard deviation) to evaluate different ACME simulations in the context of the 5^th Coupled Model Intercomparison Project (CMIP5) multi-model ensemble. In this study, we apply the PMP and the diagnostic tool to the 1 degree (ne30) v0 and v1 ACME atmosphere-only transient (1976-1999) simulations and compare the summer, winter, and annual climatologies with ~30 CMIP5 Atmosphere Model Intercomparison Project (AMIP) simulations both globally and for 5 different zonal bands. The v1 ACME Atmosphere model generally outperforms the v0 results and falls in the top half of the CMIP5 ensemble simulations.