Climate Research and Big Data
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Date
2023-11
Publication Type
Book Chapter
ETH Bibliography
yes
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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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Publication status
published
Book title
Handbook of the Philosophy of Climate Change
Journal / series
Volume
Pages / Article No.
Publisher
Springer
Event
Edition / version
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Software
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Date created
Subject
Big data; Machine learning; Climate data; Climate models; Data-driven models; Uncertainty; Predictions; Scientific understanding; Climate change; Philosophy
Organisational unit
09576 - Bresch, David Niklaus / Bresch, David Niklaus
03777 - Knutti, Reto / Knutti, Reto
Notes
Funding
167215 - Combining theory with Big Data? The case of uncertainty in prediction of trends in extreme weather and impacts (SNF)