Abstract
In recent years, the ability to gather and store information has increased dramatically, and the ability to make use of these increasing volumes of data has improved. This advent of big data has opened up new opportunities for scientific research, including for research on climate change. These changes are associated with a number of interesting philosophical questions. This chapter provides an introduction to these questions. It starts by first clarifying terminological issues concerning “big data” and related terms and by giving an overview of big data elements that can be found in climate research. Second, it discusses data in climate research with a focus on new developments regarding the increase in the volume and complexity of climate data and on how the uncertainty of climate datasets may be assessed. Finally, the chapter addresses the topic of machine learning in climate research and specifically the use of machine learning for the data-driven modeling of climate phenomena. The focus of this discussion is on the representational accuracy of data-driven models and how it might be assessed and what this implies for their use for predictions and for understanding.
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Knüsel, B., Baumberger, C., Knutti, R. (2023). Climate Research and Big Data. In: Pellegrino, G., Di Paola, M. (eds) Handbook of the Philosophy of Climate Change. Handbooks in Philosophy. Springer, Cham. https://doi.org/10.1007/978-3-031-07002-0_9
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