 Climate scientists often speak of a “hierarchy” or “spectrum” of climate models, ranging from simple to complex. The complexity of climate models increases with: the number of spatial dimensions represented; the resolution at which those dimensions are represented; the range of climate system components and processes “included” in the model; and the extent to which those processes are given realistic rather than simplified representations. As a consequence, more complex models tend to be more computationally demanding as well. The discussion below introduces a few types of model, illustrating different levels of complexity (for other types see, e.g., Stocker 2011; McGuffie &amp; Henderson-Sellars 2014). The types discussed here are physics-based, in the sense that they are grounded to a significant extent in physical theory. Data-driven or “empirical” climate models also have been developed for some purposes but are less common and will be mentioned only in passing.