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Modeling cycles and interdependence in irregularly sampled geophysical time series
Environmetrics ( IF 1.7 ) Pub Date : 2021-11-10 , DOI: 10.1002/env.2708
Granville Tunnicliffe Wilson 1 , John Haywood 2 , Lynda Petherick 3
Affiliation  

We show how an autoregressive Gaussian process model incorporating a time scale coefficient can be used to represent irregularly sampled geophysical time series. Selection of this coefficient, together with the order of autoregression, provides flexibility of the model appropriate to the structure of the data. This leads to a valuable improvement in the identification of the periodicities within and dependence between such series, which arise frequently and are often acquired at some cost in time and effort. We carefully explain the modeling procedure and demonstrate its efficacy for identifying periodic behavior in the context of an application to dust flux measurements from lake sediments in a region of subtropical eastern Australia. The model is further applied to the measurements of atmospheric carbon dioxide concentrations and temperature obtained from Antarctic ice cores. The model identifies periods in the glacial-interglacial cycles of these series that are associated with astronomical forcing, determines that they are causally related, and, by application to current measurements, confirms the prediction of climate warming.

中文翻译:

不规则采样地球物理时间序列中的建模周期和相互依赖性

我们展示了如何使用包含时间尺度系数的自回归高斯过程模型来表示不规则采样的地球物理时间序列。该系数的选择以及自回归的顺序提供了适合数据结构的模型的灵活性。这导致在识别此类系列中的周期性和之间的依赖性方面取得了有价值的改进,这些周期性经常出现并且通常需要花费一些时间和精力才能获得。我们仔细解释了建模过程,并展示了其在澳大利亚东部亚热带地区湖泊沉积物尘埃通量测量应用背景下识别周期性行为的功效。该模型进一步应用于测量从南极冰芯获得的大气二氧化碳浓度和温度。该模型确定了这些系列的冰期-间冰期循环中与天文强迫相关的时期,确定它们之间存在因果关系,并通过应用于当前的测量,证实了气候变暖的预测。
更新日期:2021-11-10
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