 The process of attribution seeks to identify the cause(s) of an observed change in climate. It employs both basic physical reasoning—observed changes are qualitatively consistent (or not) with the expected effects of a potential cause—as well as quantitative studies. The latter include fingerprint studies, in which climate models are used to simulate what would occur over a period if a particular causal factor (or set of factors) were changing, while other potential causal factors were held constant; the resulting simulated pattern of change in the target variable or field—often a spatiotemporal pattern—is the “fingerprint” of that factor (or set of factors). Scientists then perform a regression-style analysis, looking for the linear combination of the fingerprints that best fits the observations and checking whether the residual is statistically consistent with internal variability (see, e.g., Bindoff et al. 2013: Box 10.1). If so, then an estimate of the contributions of the different causal factors that were considered can be extracted from the analysis. Because of uncertainties associated with observational data, the response patterns and various methodological choices, attribution studies produce a range of estimated contributions for each  factor.[5]