Articles | Volume 14, issue 9
Atmos. Meas. Tech., 14, 6119–6135, 2021
https://doi.org/10.5194/amt-14-6119-2021
Atmos. Meas. Tech., 14, 6119–6135, 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 et al.

Data sets

ICOS Atmosphere Release 2021-1 of Level 2 Greenhouse Gas Mole Fractions of CO2, CH4, N2O, CO, meteorology and 14CO2 (1.0) ICOS Research Infrastructure https://doi.org/10.18160/WJY7-5D06

ICOS Near Real-Time (Level 1) Atmospheric Greenhouse Gas Mole Fractions of CO2, CO and CH4, growing time series starting from latest Level 2 release (Version 1.0) ICOS Research Infrastructure https://doi.org/10.18160.ATM_NRT_CO2_CH4

Model code and software

ICOS data anomaly detection algorithm - Jupyter version Alex Resovsky https://doi.org/10.5281/zenodo.5166711

hellonskis/ICOS_ATC_anomaly_detection_R: First R version (v1.0.0) Alex Resovsky https://doi.org/10.5281/zenodo.4639780

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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.