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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 (author)
Versailles Saint-Quentin-en-Yvelines University
Ramonet, Michel (author)
Versailles Saint-Quentin-en-Yvelines University
Rivier, Leonard (author)
Versailles Saint-Quentin-en-Yvelines University
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Tarniewicz, Jerome (author)
Versailles Saint-Quentin-en-Yvelines University
Ciais, Philippe (author)
Versailles Saint-Quentin-en-Yvelines University
Steinbacher, Martin (author)
Swiss Federal Laboratories for Materials Science and Technology
Mammarella, Ivan (author)
University of Helsinki
Mölder, Meelis (author)
Lund University,Lunds universitet,Institutionen för naturgeografi och ekosystemvetenskap,Naturvetenskapliga fakulteten,Dept of Physical Geography and Ecosystem Science,Faculty of Science
Heliasz, Michal (author)
Lund University,Lunds universitet,Institutionen för naturgeografi och ekosystemvetenskap,Naturvetenskapliga fakulteten,Dept of Physical Geography and Ecosystem Science,Faculty of Science
Kubistin, Dagmar (author)
German Meteorological Service (DWD)
Lindauer, Matthias (author)
German Meteorological Service (DWD)
Müller-Williams, Jennifer (author)
German Meteorological Service (DWD)
Conil, Sebastien (author)
Agence Nationale pour la gestion des Déchets RadioActifs
Engelen, Richard (author)
European Centre for Medium-range Weather Forecasts
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 (creator_code:org_t)
2021-09-17
2021
English 17 s.
In: Atmospheric Measurement Techniques. - : Copernicus GmbH. - 1867-1381 .- 1867-8548. ; 14:9, s. 6119-6135
  • Journal article (peer-reviewed)
Abstract Subject headings
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  • 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.

Subject headings

NATURVETENSKAP  -- Geovetenskap och miljövetenskap -- Klimatforskning (hsv//swe)
NATURAL SCIENCES  -- Earth and Related Environmental Sciences -- Climate Research (hsv//eng)

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art (subject category)
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