Published in

European Geosciences Union, Atmospheric Measurement Techniques, 9(14), p. 6119-6135, 2021

DOI: 10.5194/amt-14-6119-2021

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

This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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Abstract

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.