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

Resovsky, Alex ; Ramonet, Michel ; Rivier, Leonard ; Tarniewicz, Jerome ; Ciais, Philippe ; Steinbacher, Martin ; Mammarella, Ivan ; Mölder, Meelis LU ; Heliasz, Michal LU and Kubistin, Dagmar , et al. (2021) In Atmospheric Measurement Techniques 14(9). p.6119-6135
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... (More)

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.

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organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Atmospheric Measurement Techniques
volume
14
issue
9
pages
17 pages
publisher
Copernicus GmbH
external identifiers
  • scopus:85115293054
ISSN
1867-1381
DOI
10.5194/amt-14-6119-2021
language
English
LU publication?
yes
additional info
Funding Information: This research has been supported by the Commissariat l nergie atomique et aux nergies alternatives (CEA) (grant no. ECMWF/Copernicus/2016/CAMS_26). Funding Information: Acknowledgements. This work was made possible by the Copernicus Atmosphere Monitoring Service (CAMS), which is funded under regulation no.377/2014 of the European Parliament and of the Council of 3 April 2014 establishing the Copernicus program (“the Copernicus Regulation”) and operated by the ECMWF under an agreement with the European Commission dated 11 November 2014 (“ECMWF Agreement”). The authors also acknowledge the persons in charge of the 10 monitoring stations, which are part of the ICOS Atmosphere network. ICOS Atmosphere stations are supported through national consortia and funding agencies. JFJ observations are supported through ICOS Switzerland, which is funded by the Swiss National Science Foundation (grants 20FI21_148992 and 20FI20_173691) and in-house contributions. GAT, HPB and LIN observations are supported through ICOS Germany and funded by the German Ministry of Transport and Digital Infrastructure (DWD) and in-house contributions. OPE, PUY and TRN are supported through ICOS France, ANDRA, CEA and CNRS. HTM and NOR are supported by ICOS Sweden and Lund University. SMR is supported by ICOS Finland and the University of Helsinki. We also wish to thank all members of the ICOS Atmosphere Monitoring Station Assembly for their contributions to the discussions surrounding anomalous signal detection techniques. Publisher Copyright: © 2021 The Author(s). Copyright: Copyright 2021 Elsevier B.V., All rights reserved.
id
1ddc5722-2d4f-4262-82cd-ce8f3590bd7c
date added to LUP
2021-09-28 12:05:22
date last changed
2022-04-27 04:15:48
@article{1ddc5722-2d4f-4262-82cd-ce8f3590bd7c,
  abstract     = {{<p>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.</p>}},
  author       = {{Resovsky, Alex and Ramonet, Michel and Rivier, Leonard and Tarniewicz, Jerome and Ciais, Philippe and Steinbacher, Martin and Mammarella, Ivan and Mölder, Meelis and Heliasz, Michal and Kubistin, Dagmar and Lindauer, Matthias and Müller-Williams, Jennifer and Conil, Sebastien and Engelen, Richard}},
  issn         = {{1867-1381}},
  language     = {{eng}},
  month        = {{09}},
  number       = {{9}},
  pages        = {{6119--6135}},
  publisher    = {{Copernicus GmbH}},
  series       = {{Atmospheric Measurement Techniques}},
  title        = {{An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations}},
  url          = {{http://dx.doi.org/10.5194/amt-14-6119-2021}},
  doi          = {{10.5194/amt-14-6119-2021}},
  volume       = {{14}},
  year         = {{2021}},
}