 Climate models are also used as scientists seek explanations and understanding. Typically, explanations sought in climate science are causal; climate scientists seek accurate accounts of how combinations of climate system processes, conditions and features together bring about climate phenomena of interest, including climate change. Parker (2014b) identifies several ways in which climate models have facilitated such explanation: by serving as a surrogate for observations, the analysis of which can suggest explanatory hypotheses and help to fill in gaps in how-possibly/plausibly explanations; by allowing scientists to test hypotheses relevant to explanation, e.g., that a set of causal factors represented in the model is sufficient to produce a phenomenon (see also Lorenz 1970); and by serving as experimental systems that can be manipulated and studied in order to gain insight into their workings, which can inform thinking about how the climate system itself works. In connection with the latter, Held (2005) calls for increased efforts to develop and systematically study “hierarchies of lasting value”—sets of models, ranging from the highly idealized to the very complex, that are constructed such that they stand in known relations to one another so that the sources of differences in their behavior can be more readily diagnosed; he contends that the study of such hierarchies is essential to the development of climate theory in the twenty-first century.