Because weather and climate models must capture a wide variety of spatial and temporal scales, they rely heavily on parameterizations of sub-grid scale processes. The goal of this study is to demonstrate that the assumptions used to couple these parameterizations have an important effect on the climate of version 0 of the ACME model. Parameterizations in ACME are sequentially split in the sense that parameterizations are called one after another with each subsequent process feeling the effect of the preceding processes. This coupling strategy is non–commutative in the sense that the order in which processes are called impacts the solution. By examining a suite of 120 simulations with deep and shallow convection, macrophysics, microphysics and radiation parameterizations reordered, process order is shown to have a big impact on predicted climate. In particular, reordering of processes induces differences in net climate feedback that are almost as big as the inter–model spread in phase 5 of the Coupled Model Inter-comparison Project (CMIP). One reason why process ordering has such a large impact is that the effect of each process is influenced by the processes preceding it. Timing of where output is written is also an important indicator of diagnosed behavior. Application of K–means clustering demonstrates that the positioning of micro and macro physics plays a critical role on the model solution.