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.
|Journal of Agricultural, Biological and Environmental Statistics
|Published - May 11 2023
Bibliographical noteKAUST Repository Item: Exported on 2023-05-18
Acknowledgements: Fig. 2 was produced by Antonio García, scientific illustrator. We thank Dave Hart (NCAR) for the data for Fig. 1, Cecile Hannay (NCAR) for the discussion about technical details of the climate model, and the review team for comments that improved this manuscript. This research was supported by the King Abdullah University of Science and Technology (KAUST).
ASJC Scopus subject areas
- General Agricultural and Biological Sciences
- General Environmental Science
- Applied Mathematics
- Statistics and Probability
- Statistics, Probability and Uncertainty
- Agricultural and Biological Sciences (miscellaneous)