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- W3141765041 abstract "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." @default.
- W3141765041 created "2021-04-13" @default.
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- W3141765041 date "2021-09-17" @default.
- W3141765041 modified "2023-10-16" @default.
- W3141765041 title "An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations" @default.
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- W3141765041 doi "https://doi.org/10.5194/amt-14-6119-2021" @default.
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