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. We present a statistical framework to identify regional signals instation-based CO2 time series with minimal local influence. Acurve-fitting function is first applied to the detrended time series toderive a harmonic describing the annual CO2 cycle. We then combine apolynomial 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.

An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations