We propose a novel Bayesian framework for changepoint detection in large-scale spherical spatiotemporal data, with broad applicability in environmental and climate sciences. Our approach models changepoints as spatially dependent categorical variables using a multinomial probit model (MPM) with a latent Gaussian process, effectively capturing complex spatial correlation structures on the sphere. To handle the high dimensionality inherent in global datasets, we leverage stochastic partial differential equations (SPDE) and spherical harmonic transformations for efficient representation and scalable inference, drastically reducing computational burden while maintaining high accuracy. Through extensive simulation studies, we demonstrate the efficiency and robustness of the proposed method for changepoint estimation, as well as the significant computational gains achieved through the combined use of the MPM and truncated spectral representations of latent processes. Finally, we apply our method to global aerosol optical depth data, successfully identifying changepoints associated with a major atmospheric event.

翻译：我们提出了一种新颖的贝叶斯框架，用于大规模球面时空数据中的变点检测，该方法在环境与气候科学领域具有广泛适用性。我们的方法通过采用带有潜高斯过程的多项式概率模型（MPM），将变点建模为空间相关的分类变量，从而有效捕捉球面上复杂的空间相关结构。为处理全球数据集固有的高维特性，我们利用随机偏微分方程（SPDE）和球谐变换进行高效表示与可扩展推理，在保持高精度的同时显著降低了计算负担。通过大量的模拟研究，我们证明了所提方法在变点估计方面的效率与鲁棒性，以及通过结合使用MPM与潜过程的截断谱表示所实现的显著计算增益。最后，我们将该方法应用于全球气溶胶光学厚度数据，成功识别了与一次重大大气事件相关的变点。