Discussion on “Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach”

Julie Bessac (), Robert Underwood and Sheng Di
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Julie Bessac: Computational Science Center
Robert Underwood: Computational Science Center
Sheng Di: Computational Science Center

Journal of Agricultural, Biological and Environmental Statistics, 2023, vol. 28, issue 2, No 11, 358-364

Abstract: Abstract We thank the authors for this interesting paper that highlights important ideas and concepts for the future of climate model ensembles and their storage, as well as future uses of stochastic emulators. Stochastic emulators are particularly relevant because of the statistical nature of climate model ensembles, as discussed in previous work of the authors (Castruccio et al. in J Clim 32:8511–8522, 2019; Hu and Castruccio in J Clim 34:8409–8418, 2021). We thank the authors for sharing of some of their data with us in order to illustrate this discussion. In the following, in Sect. 1 we discuss alternative techniques currently used and studied, namely lossy compression and ideas emerging from the climate modeling community, that could feed the discussion on ensemble and storage. In that section, we also present numerical results of compression performed on the data shared by the authors. In Sect. 2, we discuss the current statistical model proposed by the authors and its context. We discuss other potential uses of stochastic emulators in climate and Earth modeling.

Keywords: Lossy Compression; Stochastic Emulators; Climate model outputs (search for similar items in EconPapers)
Date: 2023
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DOI: 10.1007/s13253-023-00540-7

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