 In support of the consensus position regarding detection and attribution, Oreskes (2007) argues that research underpinning it accords with various models of scientific reliability: it has a strong inductive base, it has made nontrivial correct predictions, it has survived persistent efforts at falsification, it has uncovered a body of evidence that is consilient, and—in light of all of this—climate scientists have concluded that the best explanation of recent observed warming includes a significant anthropogenic component. She also emphasizes that the core idea that increased atmospheric concentrations of carbon dioxide would warm the climate emerged first from basic physical understanding, not from computer models of the climate system. In a complementary analysis, Lloyd (2010, 2013) offers a defense of the use of climate models in attribution research and argues that studies involving multiple climate models—which incorporate similar assumptions about the radiative properties of greenhouse gases but differ somewhat in their empirically-supported representations of other climate system processes—provide enhanced support for the conclusion that greenhouse gases are (part of) a good explanation of observed recent warming; she argues that such studies illustrate a distinctive kind of model robustness, which incorporates variety-of-evidence considerations, and that such robustness is a “confirmatory virtue” (see also Vezér 2016b). Robustness and variety-of-evidence considerations are also highlighted by Parker (2010), in a different way; she suggests that they have been important for justifying detection and attribution claims, given that formal studies, such as fingerprint studies, often rely on contentious or false assumptions. Winsberg (2018: Ch.11&amp;12) critically examines and synthesizes much of this recent work on robustness in climate science, drawing on Schupbach’s (2016) account of robustness analysis as explanatory reasoning.