A piecewise linear model for detecting climatic trends and their structural changes with application to mesosphere/lower thermosphere winds over Collm, Germany
A piecewise linear model for detecting climatic trends and their structural changes with application to mesosphere/lower thermosphere winds over Collm, Germany
复制标题
DOI：
10.1029/2010jd014080
复制
发表时间：
2010-11
影响因子：
--
通讯作者：
R. Liu;C. Jacobi;P. Hoffmann;G. Stober;E. Merzlyakov
中科院分区：
文献类型：
--
作者：
R. Liu;C. Jacobi;P. Hoffmann;G. Stober;E. Merzlyakov
[1]建立了一个分段线性模型，用于在已知断点个数和位置的情况下，检测时间序列中的气候趋势及其结构变化。离开（即，初始噪声项）被允许通过一阶和二阶自回归模型来解释。如果残差被接受为正态分布的白色噪声，则使用施瓦茨贝叶斯信息准则（BIC）评估候选模型的拟合优度。采用蒙特-卡罗法估计了所有趋势参数的不确定性。该模型适用于中层和低热层（MLT）风在科尔姆，德国，在1960年至2007年。1980年后，纬向盛行风的持续增加，观察到在所有的季节，因此在纬向年平均的基础上的主要模式。不同季节的纬向盛行风变化趋势不同。根据BIC，在年平均纬向风和纬向风中确定了几个主要的趋势BP。然而，鉴于20世纪70年代末之前的风变率很大，考虑了替代模型。这提供了四个额外的小中断。在某些情况下，初始噪声必须通过自回归模型进一步解释，这表明其他未识别的因素也可能发挥作用。
[1] A piecewise linear model is developed to detect climatic trends and their structural changes in time series with a priori unknown number and positions of breakpoints (BPs). The departure (i.e., the initial noise term) of trends from time series is allowed to be interpreted by the first- and second-order autoregressive models. The goodness of fit of candidate models, if the residuals are accepted as normally distributed white noise, is evaluated using the Schwarz Bayesian Information Criterion (BIC). The uncertainties of all trend parameters are estimated using the Monte-Carlo method. The model is applied to the mesosphere and lower thermosphere (MLT) winds obtained at Collm, Germany, during 1960–2007. A persistent increase after ∼1980 of the zonal prevailing wind is observed in all seasons and hence in the zonal annual mean based on the primary models. Trends of the meridional prevailing wind are different for different seasons. Several major trend BPs are identified in the annual mean zonal and meridional winds according to BIC. However, in view of the large wind variability before the late 1970s, alternative models are considered. This provides four additional minor breaks. In some cases, the initial noise must be further interpreted by autoregressive models, suggesting that other unidentified factors may also play a role.