Suchergebnisse

Cover -- Contents -- List of Abbreviations -- List of Figures -- 1. Introduction -- 2. Some preliminary remarks -- 2.1 Epistemic challenges of highly complex systems -- 2.2 Discovery and justification: the DJ distinction -- 2.3 A few words about objectivity -- 3. Three ideals of science -- 3.1 Value‐free science -- 3.1.1 Introduction: values in science -- 3.1.1.1 The rise and fall of the value‐free ideal -- 3.1.1.2 Epistemic versus non‐epistemic values -- 3.1.2 Inductive risks and social values -- 3.1.2.1 Social values and methodological considerations -- 3.1.3 Social values in climate science -- 3.1.3.1 Unconstrained decision making, predictive preferences andcostrestrictions -- 3.1.3.2 Non‐traceability -- 3.1.3.3 Coarser uncertainty quantification and other possiblecounterarguments -- 3.1.3.4 Systematic bias and wishful thinking -- 3.1.4 Conclusion -- 3.2 Model, theory and observation -- 3.2.1 Introduction: from handmaiden to a life of their own -- 3.2.1.2 Observation -- 3.2.2 Theory‐ladenness, underdetermination and models of data -- 3.2.2.1 Models of data -- 3.2.3 Observations in climate science -- 3.2.3.1 Climate data -- 3.2.3.1.1 Observations and uncertainties -- 3.2.3.1.2 Satellite data -- 3.2.3.1.3 Paleoclimate data and proxies -- 3.2.3.1.4 Reanalysis data -- 3.2.3.2 Model‐data interdependency -- 3.2.3.3 Verification and validation -- 3.2.4 Conclusion -- 3.3 Predictability -- 3.3.1 Introduction: predictability and uncertainty -- 3.3.2 Robustness -- 3.3.3 Uncertainties in climate science -- 3.3.3.1 Numerical approximation and structural uncertainty -- 3.3.3.2 Parameter uncertainty -- 3.3.3.3 Second‐order uncertainty -- 3.3.3.3.1 Ensemble studies -- 3.3.3.3.2 The quantification problem -- 3.3.3.4 Robustness revisited -- 3.3.4 Conclusion -- 3.4 Looking back and a tentative look forward -- 3.4.1 Complexity and understanding.

Ever since climate change has been identified as one of the most significant challenges of humanity, climate change deniers have widely tried to discredit the work of scientists. To show how these processes work, Maria M. Sojka examines three ideals about how science should operate. These ideals concern the understanding of uncertainties, the relationship between models and data, and the role of values in science. Their widespread presence in the public understanding of science makes it easy for political and industrial stakeholders to undermine inconvenient research. To address this issue, Sojka analyses the importance of tacit knowledge in scientific practice and the question what defines an expert