Articles | Volume 14, issue 9
https://doi.org/10.5194/amt-14-6119-2021
https://doi.org/10.5194/amt-14-6119-2021
Research article
 | 
17 Sep 2021
Research article |  | 17 Sep 2021

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

Alex Resovsky, Michel Ramonet, Leonard Rivier, Jerome Tarniewicz, Philippe Ciais, Martin Steinbacher, Ivan Mammarella, Meelis Mölder, Michal Heliasz, Dagmar Kubistin, Matthias Lindauer, Jennifer Müller-Williams, Sebastien Conil, and Richard Engelen

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Latest update: 21 Apr 2024
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Short summary
We present a technical description of a statistical methodology for extracting synoptic- and seasonal-length anomalies from greenhouse gas time series. The definition of what represents an anomalous signal is somewhat subjective, which we touch on throughout the paper. We show, however, that the method performs reasonably well in extracting portions of time series influenced by significant North Atlantic Oscillation weather episodes and continent-wide terrestrial biospheric aberrations.