在由周年和半年谐波驱动的原型季节温度循环中的空间和时间移动
On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi-annual harmonics
摘要：
需要用统计方法来评估和量化气候变化中陆地和海洋表面温度等环境过程的不确定性。通常，年度谐波用于表征季节性温度周期的变化。然而，气候季节周期的一个经常被忽视的特征是半年谐波，它可以解释季节周期方差的很大一部分，并且在空间上的振幅和相位各不相同。总之，一年和半年谐波的空间变化可以在驱动与季节性相关的过程中发挥重要作用（例如，生态和农业）。我们提出了一个多变量的时空模型来量化的空间和时间变化的最低和最高温度的季节性周期的函数的年度和半年谐波。我们的方法捕捉空间依赖性，时间动态，和多变量依赖这些谐波通过空间和时间变化的系数。我们将该模型应用于1979年至2018年北美的最低和最高温度。贝叶斯范式内的正式模型推理，使识别区域经历显着变化的最低和最高温度的季节性周期，由于在两个谐波的变化的相对影响。
英文摘要：
Statistical methods are required to evaluate and quantify the uncertainty in environmental processes, such as land and sea surface temperature, in a changing climate. Typically, annual harmonics are used to characterize the variation in the seasonal temperature cycle. However, an often overlooked feature of the climate seasonal cycle is the semi-annual harmonic, which can account for a significant portion of the variance of the seasonal cycle and varies in amplitude and phase across space. Together, the spatial variation in the annual and semi-annual harmonics can play an important role in driving processes that are tied to seasonality (e.g., ecological and agricultural processes). We propose a multivariate spatio-temporal model to quantify the spatial and temporal change in minimum and maximum temperature seasonal cycles as a function of the annual and semi-annual harmonics. Our approach captures spatial dependence, temporal dynamics, and multivariate dependence of these harmonics through spatially and temporally-varying coefficients. We apply the model to minimum and maximum temperature over North American for the years 1979 to 2018. Formal model inference within the Bayesian paradigm enables the identification of regions experiencing significant changes in minimum and maximum temperature seasonal cycles due to the relative effects of changes in the two harmonics.
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