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Compression of climate simulations with a nonstationary global spatiotemporal SPDE model. (English) Zbl 1446.62139
Summary: 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 statistical model can be used as a means to mitigate the storage burden by providing a stochastic approximation of the climate simulations. Indeed, if a suitably validated statistical model can be formulated to draw realizations whose spatiotemporal structure is similar to that of the original computer simulations, then 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 available for compression. Once the model is trained, surrogate runs can be instantaneously generated on a laptop by storing just 20 Megabytes of parameters as opposed to more than six Gigabytes of the original ensemble.
MSC:
62H11 Directional data; spatial statistics
60H15 Stochastic partial differential equations (aspects of stochastic analysis)
62-08 Computational methods for problems pertaining to statistics
62P12 Applications of statistics to environmental and related topics
86A08 Climate science and climate modeling
62R07 Statistical aspects of big data and data science
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