Compression of Climate Simulations with a Nonstationary Global Spatio-Temporal SPDE Model

• Activity Number: 450 - Uncertainty Quantification for Environmental Applications
• Type: Topic Contributed
• Date/Time: Thursday, August 6, 2020 : 10:00 AM to 11:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Stefano Castruccio* and Geir-Arne Fuglstad
• Companies: University of Notre Dame and Norwegian University of Science and Technology
• Keywords: Stochastic Partial Differential Equation; global data; sphere; nonstationarity; climate models

Abstract

Modern climate models pose an ever-increasing storage burden to computational facilities, and the upcoming generation of global simulations from the next Intergovernmental Panel on Climate Change will require a substantial share of the budget of research centers worldwide to be allocated just for this task. A suitably validated statistical model can be formulated to draw realizations whose spatio-temporal structure is similar to that of the original computer simulations, and the estimated parameters are effectively all the information that needs to be stored. In this work, we propose a new statistical model defined via a stochastic partial differential equation (SPDE) on the sphere and in evolving time. The model is able to capture nonstationarities across latitudes, longitudes and land/ocean domains for more than 300 million data points, while also overcoming the fundamental limitations of current global statistical models.