Abstract

The continued development of General Circulation Models (GCMs) towards increasing resolution and complexity is a predominantly chosen strategy to advance climate science, resulting in channelling of research and funding to meet this aspiration. Yet many other modelling strategies have also been developed and can be used to understand past and present climates, to project future climates and ultimately to support decision-making. We argue that a plurality of climate modelling strategies and an equitable distribution of funding among them would be an improvement on the current predominant strategy for informing policy making. To support our claim, we use a philosophy of science approach to compare increasing resolution and complexity of General Circulation Models with three alternate examples: the use of machine learning techniques, the physical climate storyline approach, and Earth System Models of Intermediate Complexity. We show that each of these strategies prioritises a particular set of methodological aims, among which are empirical agreement, realism, comprehensiveness, diversity of process representations, inclusion of the human dimension, reduction of computational expense, and intelligibility. Thus, each strategy may provide adequate information to support different specific kinds of research and decision questions. We conclude that, because climate decision-making consists of different kinds of questions, many modelling strategies are all potentially useful, and can be used in a complementary way.