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.

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Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on amt-2021-16', Anonymous Referee #2, 24 Mar 2021
    • AC1: 'Reply on RC1', Alex Resovsky, 14 Apr 2021
  • RC2: 'Comment on amt-2021-16', Anonymous Referee #1, 10 May 2021
    • AC2: 'Reply on RC2', Alex Resovsky, 05 Jun 2021

Peer review completion

AR: Author's response | RR: Referee report | ED: Editor decision
AR by Sarah Buchmann on behalf of the Authors (29 Jul 2021)  Author's response
ED: Publish as is (30 Jul 2021) by Can Li
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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.