Sustainably Modeling a Sustainable Future Climate

Voluminous highly resolved climate data requires high-performance statistical modeling frameworks that are both accurate and sustainable on modern supercomputing platforms. Spatio-temporal modeling methods, such as Maximum Likelihood Estimation (MLE), primarily rely on performing a dense Cholesky factorization of large covariance matrices, which becomes a significant bottleneck at scale. We present an enhanced version of ExaGeoStat, a high-performance geostatistical modeling framework, designed to address this challenge through tile-based matrix compression, mixed-precision arithmetic, and architecture-aware scheduling. Building upon our previous work on Fugaku, an Arm A64FX system, we extend our results to three supercomputers featuring diverse hardware architectures: Frontier (AMD MI250X GPUs), Alps (NVIDIA GH200 GPUs), and Shaheen III (AMD EPYC Genoa CPUs). We demonstrate that adaptive algorithms targeting low-rank and low-precision opportunities can deliver up to 4× speedup, 2× memory savings, and over 70% energy reduction while maintaining application-acceptable accuracy. Our work demonstrates the feasibility of running accurate climate-emulation workloads on next-generation AI hardware in a power-efficient manner, thereby advancing the sustainability of climate data science itself.