Philosophy and Climate Science

Description

There continues to be a vigorous public debate in our society about the status of climate science. Much of the skepticism voiced in this debate suffers from a lack of understanding of how the science works - in particular the complex interdisciplinary scientific modeling activities such as those which are at the heart of climate science. In this book Eric Winsberg shows clearly and accessibly how philosophy of science can contribute to our understanding of climate science, and how it can also shape climate policy debates and provide a starting point for research. Covering a wide range of topics including the nature of scientific data, modeling, and simulation, his book provides a detailed guide for those willing to look beyond ideological proclamations, and enriches our understanding of how climate science relates to important concepts such as chaos, unpredictability, and the extent of what we know. Contents List of Figures List of Boxes List of Tables Preface 1 Introduction 2 Data 2.1 Introduction 2.2 The Nature of Scientific Data 2.3 Evidence of Warming 2.4 Satellite Data: A Cautionary Tale 2.5 Evidence of Warming, Continued 2.6 Conclusion Suggestions for Further Reading 3 Models 3.1 Introduction 3.2 Energy Balance Models 3.3 The Nature of Models: Mediating Models 3.4 Conclusion: How Good Are Energy Balance Models? Suggestions for Further Reading 4 Simulations 4.1 Introduction 4.2 What Is Computer Simulation? 4.3 The Purposes of Climate Modeling 4.4 Features of Computer Simulations of the Climate Suggestions for Further Reading 5 Chaos 5.1 Introduction 5.2 What Is Chaos? 5.3 Two Toy Chaotic Models 5.4 Mitigating Butterflies 5.5 The Joint Weather/ Climate Strategy 5.6 The Hawkmoth Effect 5.7 Conclusion Suggestions for Further Reading 6 Probability 6.1 Introduction 6.2 Interpretations of Probability for Climate Science 6.3 Chances and Credences 6.4 … And How to Tell Them Apart 6.5 Probabilities in Weather and Climate 6.6 Conclusion Suggestions for Further Reading 7 Confidence 7.1 Introduction 7.2 Sources of Uncertainty 7.3 How Uncertainty Is Quantified I: Ensembles and Sampling 7.4 How Uncertainty Is Quantified II: Expert Elicitation 7.5 Group Credences 7.6 Confidence 7.7 Conclusion Suggestions for Further Reading 8 Decision 8.1 Statistical Inference: Two Paradigms 8.2 Probability and Inference 8.3 Risk and Uncertainty – Three Approaches 8.4 Probability and Decision 8.5 Cost Benefit Analysis 8.6 Conclusion Suggestions for Further Reading 9 Values 9.1 Introduction 9.2 Risk 9.3 Replies to the BRAIR 9.4 A Model-Specific Response to BRAIR: Prediction Preference 9.5 Models, Values, and Probabilities 9.6 Imprecise Probabilities 9.7 Values and Biases 9.8 Conclusion Suggestions for Further Reading 10 Skill 10.1 Introduction 10.2 Verification and Validation 10.3 An Alternative Picture 10.4 Tuning Revisited 10.5 Conclusion Suggestions for Further Reading 11 Robustness 11.1 Introduction 11.2 Robustness Analysis – A Focus on Models 11.3 Robustness Analysis in Climate Science: Lloyd and Parker 11.4 Robustness Analysis – Beyond Model Agreement 11.5 Sufficient Diversity of Evidence – Cumulative Epistemic Power 11.6 Conclusion: RA Diversity Suggestions for Further Reading 12 Diversity 12.1 Introduction 12.2 Explanatory Robustness 12.3 Evidence of an ECS Hypothesis: Solely from Model Diversity 12.4 Evidence of an ECS Hypothesis: Beyond Model Diversity 12.5 Conclusion Suggestions for Further Reading 13 Social Epistemology 13.1 Introduction 13.2 First Branch: Social Interaction 13.3 Second Branch: From Individuals to Groups 13.4 Third Branch: Institutional Arrangements Suggestions for Further Reading 14 Epilogue Appendix: Structural Stability and the “Hawkmoth Effect” A.1 What Is the Hawkmoth Effect Supposed to Be? A.2 Two Routes to a Hawkmoth Effect A.3 The Structural Stability Route A.4 The Demon’s Apprentice Route A.5 Small Arbitrary Model Perturbations References Index