A Multivariate Global Spatiotemporal Stochastic Generator for Climate Ensembles

Matthew Edwards (), Stefano Castruccio () and Dorit Hammerling ()
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Matthew Edwards: Newcastle University
Stefano Castruccio: University of Notre Dame
Dorit Hammerling: National Center for Atmospheric Research

Journal of Agricultural, Biological and Environmental Statistics, 2019, vol. 24, issue 3, No 5, 464-483

Abstract: Abstract In order to understand and quantify the uncertainties in projections and physics of a climate model, a collection of climate simulations (an ensemble) is typically used. Given the high-dimensionality of the input space of a climate model, as well as the complex, nonlinear relationships between the climate variables, a large ensemble is often required to accurately assess these uncertainties. If only a small number of climate variables are of interest at a specified spatial and temporal scale, the computational and storage expenses can be substantially reduced by training a statistical model on a small ensemble. The statistical model then acts as a stochastic generator (SG) able to simulate a large ensemble, given a small training ensemble. Previous work on SGs has focused on modeling and simulating individual climate variables (e.g., surface temperature, wind speed) independently. Here, we introduce a SG that jointly simulates three key climate variables. The model is based on a multistage spectral approach that allows for inference of more than 80 million data points for a nonstationary global model, by conducting inference in stages and leveraging large-scale parallelization across many processors. We demonstrate the feasibility of jointly simulating climate variables by training the SG on five ensemble members from a large ensemble project and assess the SG simulations by comparing them to the ensemble members not used in training. Supplementary materials accompanying this paper appear online.

Keywords: Nonstationary; Massive data; Stepwise estimation; Parallel computation (search for similar items in EconPapers)
Date: 2019
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DOI: 10.1007/s13253-019-00352-8

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