Preprints
https://doi.org/10.5194/amt-2021-16
https://doi.org/10.5194/amt-2021-16

  09 Mar 2021

09 Mar 2021

Review status: a revised version of this preprint is currently under review for the journal AMT.

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

Alex Resovsky1, Michel Ramonet1, Leonard Rivier1, Jerome Tarniewicz1, Philippe Ciais1, Martin Steinbacher2, Ivan Mammarella3, Meelis Mölder4, Michal Heliasz4, Dagmar Kubistin5, Matthias Lindauer5, Jennifer Müller-Williams5, Sebastien Conil6, and Richard Engelen7 Alex Resovsky et al.
  • 1Laboratoire des Sciences du Climat et de l’Environnement , LSCE/IPSL, CEA-CNRS-UVSQ, Université Paris-Saclay, 91191 Gif-sur-Yvette, France
  • 2Empa, Laboratory for Air Pollution/Environmental Technology, CH-8600 Duebendorf, Switzerland
  • 3Institute for Atmospheric and Earth System Research/ Physics, University of Helsinki, Helsinki, Finland
  • 4Lund University, 22100 Lund, Sweden
  • 5Deutscher Wetterdienst, Meteorological Observatory Hohenpeissenberg, 82383 Hohenpeissenberg, Germany
  • 6DRD/OPE, Andra, Bure, 55290, France
  • 7European Center for Medium-Range Weather Forecasts, Shinfield Park, Reading, UK

Abstract. We present a statistical framework for near real-time signal processing to identify regional signals in CO2 time series recorded at stations which are normally uninfluenced by local processes. 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 two standard deviations of the smoothed cycle about the annual cycle. Spikes in the smoothed daily data which rise above this 2σ threshold are classified as anomalies. Examining patterns of anomalous behavior across multiple sites allows us to quantify the impacts of synoptic-scale weather events and better understand the regional carbon cycling implications of extreme seasonal occurrences such as droughts.

Alex Resovsky et al.

Status: final response (author comments only)

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

Alex Resovsky et al.

Alex Resovsky et al.

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Short summary
We present a technical description of a statistical methodology for extracting synoptic and seasonal length anomalies from CO2 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 the time series influenced by significant NAO+ weather episodes and continent-wide terrestrial biospheric aberrations.