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.
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We thank C. Beisbart, A. Merrifield, S. Sippel, R. McMahon and J. Lilliestam for discussions and comments that have improved the quality of this manuscript. The research was supported by the Swiss National Science Foundation, National Research Programme 75 Big Data, project no. 167215.
The authors declare no competing interests.
Journal peer review information: Nature Climate Change thanks Prabhat, Wendy Parker and other anonymous reviewer(s) for their contribution to this work.
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Knüsel, B., Zumwald, M., Baumberger, C. et al. Applying big data beyond small problems in climate research. Nat. Clim. Chang. 9, 196–202 (2019). https://doi.org/10.1038/s41558-019-0404-1
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