Saving Storage in Climate Ensembles: A Model-Based Stochastic Approach

Huang Huang, Stefano Castruccio, Allison H. Baker and Marc G. Genton ()
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Huang Huang: King Abdullah University of Science and Technology
Stefano Castruccio: University of Notre Dame
Allison H. Baker: National Center for Atmospheric Research
Marc G. Genton: King Abdullah University of Science and Technology

Journal of Agricultural, Biological and Environmental Statistics, 2023, vol. 28, issue 2, No 7, 324-344

Abstract: Abstract While climate models are an invaluable tool for increasing our understanding and therefore, the predictability of the Earth’s system for decades, their increase in complexity and resolution has put a considerable, growing strain on the computational resources of research centers and institutions worldwide. The statistics community has a long history of developing stochastic models as a means to save computational time, but the emergence of storage as an additional cost for climate investigations has prompted a reformulation of the aim of statistical models in model-based environmental science. Can stochastic approximations be useful as a mechanism for saving both computational time and storage? We focus on a collection of simulations from a climate model and propose several statistical models of increasing complexity. By analyzing and discussing the associated costs for each model, we demonstrate how computation and storage are closely intertwined, and how a statistical model of increasing complexity is justified only to the extent that information at a fine spatial and/or temporal scale is sought to be preserved.Supplementary materials accompanying this paper appear online.

Keywords: Climate model; Computational cost; Global model; Space-time model; Stochastic generator; Storage cost (search for similar items in EconPapers)
Date: 2023
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Citations: View citations in EconPapers (1)

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DOI: 10.1007/s13253-022-00518-x

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