论文标题
在年度和半年度谐波驱动的原型季节性温度周期的空间和时间变化上
On the spatial and temporal shift in the archetypal seasonal temperature cycle as driven by annual and semi-annual harmonics
论文作者
论文摘要
需要统计方法来评估和量化环境过程中的不确定性，例如土地和海面温度在变化的气候下。通常，每年的谐波用于表征季节性温度周期中的变化。但是，气候季节周期的一个经常被忽略的特征是半年度谐波，它可以解释季节周期的差异的很大一部分，并且在整个空间之间的幅度和相位变化。同时，年度和半年度谐波中的空间变化在与季节性相关的驱动过程中起着重要作用（例如生态和农业过程）。我们提出了一个多元时空模型，以量化最低和最高温度季节性周期的空间和时间变化，这是年度和半年度谐波的函数。我们的方法通过空间和随时间变化的系数捕获了这些谐波的空间依赖性，时间动力学和多元依赖性。我们将模型应用于1979年至2018年的北美地区的最低温度和最高温度。贝叶斯范式内的正式模型推断能够鉴定出由于两种谐波变化的相对影响而导致的最低和最高温度季节性周期发生显着变化的区域。
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.
