We present a statistical framework to identify regional signals in station-based CO₂ time series with minimal local influence. A curve-fitting function is first applied to the detrended time series to derive a harmonic describing the annual CO₂ cycle. We then combine a polynomial fit to the data with a short-term residual filter to estimate the smoothed cycle and define a seasonally adjusted noise component, equal to 2 standard deviations of the smoothed cycle about the annual cycle. Spikes in the smoothed daily data which surpass this ±2σ threshold are classified as anomalies. Examining patterns of anomalous behavior across multiple sites allows us to quantify the impacts of synoptic-scale atmospheric transport events and better understand the regional carbon cycling implications of extreme seasonal occurrences such as droughts.

中文翻译：

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一种检测欧洲高塔和山地站温室气体时间序列中非背景信号的算法

我们提出了一个统计框架来识别基于站点的 CO ₂时间序列中具有最小局部影响的区域信号。首先将曲线拟合函数应用于去趋势时间序列，以推导出描述年度 CO ₂循环的谐波。然后，我们将多项式拟合数据与短期残差滤波器相结合，以估计平滑周期并定义季节性调整的噪声分量，等于年周期平滑周期的 2 个标准差。超过这个±2 σ的平滑每日数据中的峰值阈值被归类为异常。检查多个地点的异常行为模式使我们能够量化天气尺度大气传输事件的影响，并更好地了解干旱等极端季节性事件对区域碳循环的影响。