Christoph Baumberger

Bio: Christoph Baumberger is an academic researcher from ETH Zurich. The author has contributed to research in topics: Explication & Reflective equilibrium. The author has an hindex of 7, co-authored 23 publications receiving 274 citations. Previous affiliations of Christoph Baumberger include London School of Economics and Political Science.

Building confidence in climate model projections: an analysis of inferences from fit

Abstract: Climate model projections are used to inform policy decisions and constitute a major focus of climate research. Confidence in climate projections relies on the adequacy of climate models for those projections. The question of how to argue for the adequacy of models for climate projections has not gotten sufficient attention in the climate modeling community. The most common way to evaluate a climate model is to assess in a quantitative way degrees of ‘model fit’; that is, how well model results fit observation-based data (empirical accuracy) and agree with other models or model versions (robustness). However, such assessments are largely silent about what those degrees of fit imply for a model's adequacy for projecting future climate. We provide a conceptual framework for discussing the evaluation of the adequacy of models for climate projections. Drawing on literature from philosophy of science and climate science, we discuss the potential and limits of inferences from model fit. We suggest that support of a model by background knowledge is an additional consideration that can be appealed to in arguments for a model's adequacy for long-term projections, and that this should explicitly be spelled out. Empirical accuracy, robustness and support by background knowledge neither individually nor collectively constitute sufficient conditions in a strict sense for a model's adequacy for long-term projections. However, they provide reasons that can be strengthened by additional information and thus contribute to a complex non-deductive argument for the adequacy of a climate model or a family of models for long-term climate projections. WIREs Clim Change 2017, 8:e454. doi: 10.1002/wcc.454 For further resources related to this article, please visit the WIREs website.

What Is Understanding? An Overview of Recent Debates in Epistemology and Philosophy of Science

Abstract: The paper provides a systematic overview of recent debates in epistemology and philosophy of science on the nature of understanding. We explain why philosophers have turned their attention to understanding and discuss conditions for “explanatory” understanding of why something is the case and for “objectual” understanding of a whole subject matter. The most debated conditions for these types of understanding roughly resemble the three traditional conditions for knowledge: truth, justification and belief. We discuss prominent views about how to construe these conditions for understanding, whether understanding indeed requires conditions of all three types and whether additional conditions are needed.

Applying big data beyond small problems in climate research

Abstract: Commercial success of big data has led to speculation that big-data-like reasoning could partly replace theory-based approaches in science. Big data typically has been applied to ‘small problems’, which are well-structured cases characterized by repeated evaluation of predictions. Here, we show that in climate research, intermediate categories exist between classical domain science and big data, and that big-data elements have also been applied without the possibility of repeated evaluation. Big-data elements can be useful for climate research beyond small problems if combined with more traditional approaches based on domain-specific knowledge. The biggest potential for big-data elements, we argue, lies in socioeconomic climate research. Big data is increasingly popular in many research domains. This Perspective discusses where elements of big data approaches have been employed in climate research and where combining big data with theory-driven research can be most fruitful.

Explicating Objectual Understanding: Taking Degrees Seriously

Abstract: The paper argues that an account of understanding should take the form of a Carnapian explication and acknowledge that understanding comes in degrees. An explication of objectual understanding is defended, which helps to make sense of the cognitive achievements and goals of science. The explication combines a necessary condition with three evaluative dimensions: an epistemic agent understands a subject matter by means of a theory only if the agent commits herself sufficiently to the theory of the subject matter, and to the degree that the agent grasps the theory (i.e., is able to make use of it), the theory answers to the facts and the agent’s commitment to the theory is justified. The threshold for outright attributions of understanding is determined contextually. The explication has descriptive as well as normative facets and allows for the possibility of understanding by means of non-explanatory (e.g., purely classificatory) theories.

Future climate risk from compound events

Abstract: Floods, wildfires, heatwaves and droughts often result from a combination of interacting physical processes across multiple spatial and temporal scales. The combination of processes (climate drivers and hazards) leading to a significant impact is referred to as a ‘compound event’. Traditional risk assessment methods typically only consider one driver and/or hazard at a time, potentially leading to underestimation of risk, as the processes that cause extreme events often interact and are spatially and/or temporally dependent. Here we show how a better understanding of compound events may improve projections of potential high-impact events, and can provide a bridge between climate scientists, engineers, social scientists, impact modellers and decision-makers, who need to work closely together to understand these complex events.

The Scientific Image

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Knowledge in a Social World

Abstract: Book Information Knowledge in a Social World. By Alvin I. Goldman. Clarendon Press. Oxford. 1999. Pp. xiii + 407. Paperback, £16.99.