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

Abstract

Abstract. We present a statistical framework to identify regional signals in\nstation-based CO2 time series with minimal local influence. A\ncurve-fitting function is first applied to the detrended time series to\nderive a harmonic describing the annual CO2 cycle. We then combine a\npolynomial 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.\n