Abstract
Earth system models (ESMs) are fundamental for understanding Earth's complex climate system. However, the computational demands and storage requirements of ESM simulations limit their utility. For the newly published CESM2-LENS2 data, which suffer from this issue, we propose a novel stochastic generator (SG) as a practical complement to the CESM2, capable of rapidly producing emulations closely mirroring training simulations. Our SG leverages the spherical harmonic transformation (SHT) to shift from spatial to spectral domains, enabling efficient low-rank approximations that significantly reduce computational and storage costs. By accounting for axial symmetry and retaining distinct ranks for land and ocean regions, our SG captures intricate non-stationary spatial dependencies. Additionally, a modified Tukey g-and-h (TGH) transformation accommodates non-Gaussianity in high-temporal-resolution data. We apply the proposed SG to generate emulations for surface temperature simulations from the CESM2-LENS2 data across various scales, marking the first attempt of reproducing daily data. These emulations are then meticulously validated against training simulations. This work offers a promising complementary pathway for efficient climate modeling and analysis while overcoming computational and storage limitations.
Brief Biography
Yan is a Postdoctoral Fellow in Statistics at King Abdullah University of Science and Technology (KAUST), affiliated with the Spatio-Temporal Statistics and Data Science (STSDS) research group led by Prof. Genton. I received my Ph.D. degree in Statistics from Renmin University of China in 2023. During my doctoral studies, I undertook research visits to Hong Kong Baptist University and KAUST. I received my B.S. degree in Statistics from Beijing Institute of Technology, China, 2018.
My research interests include spatio-temporal statistics, subsampling methods, nonparametric statistics, and computational statistics and HPC. My work primarily focuses on spatio-temporal statistics, particularly in the analysis of large-scale spatio-temporal data from Climate and Environmental Sciences and the development of large- and exa-scale climate emulators, with Gaussian processes being key tools. I have also developed subsampling techniques for various data types and statistical models, with an emphasis on nonparametric statistics.
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