2020/03/15 by Joshua S. North, Erin M. Schliep, North, Joshua S. +3 · 1 citation
Economics, Econometrics and Finance · Environmental Science · #Applications (stat.AP) #Atmospheric and Environmental Gas Dynamics #Climate Change Policy and Economics #Climate variability and models #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.2003.06924
openalex publication_date 2020/03/15 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
Statistical methods are required to evaluate and quantify the uncertainty in\nenvironmental processes, such as land and sea surface temperature, in a\nchanging climate. Typically, annual harmonics are used to characterize the\nvariation in the seasonal temperature cycle. However, an often overlooked\nfeature of the climate seasonal cycle is the semi-annual harmonic, which can\naccount for a significant portion of the variance of the seasonal cycle and\nvaries in amplitude and phase across space. Together, the spatial variation in\nthe annual and semi-annual harmonics can play an important role in driving\nprocesses that are tied to seasonality (e.g., ecological and agricultural\nprocesses). We propose a multivariate spatio-temporal model to quantify the\nspatial and temporal change in minimum and maximum temperature seasonal cycles\nas a function of the annual and semi-annual harmonics. Our approach captures\nspatial dependence, temporal dynamics, and multivariate dependence of these\nharmonics through spatially and temporally-varying coefficients. We apply the\nmodel to minimum and maximum temperature over North American for the years 1979\nto 2018. Formal model inference within the Bayesian paradigm enables the\nidentification of regions experiencing significant changes in minimum and\nmaximum temperature seasonal cycles due to the relative effects of changes in\nthe two harmonics.\n