Michal Heliasz
Research engineer
An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations
Author
Summary, in English
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.
Department/s
- Dept of Physical Geography and Ecosystem Science
Publishing year
2021-09-17
Language
English
Pages
6119-6135
Publication/Series
Atmospheric Measurement Techniques
Volume
14
Issue
9
Document type
Journal article
Publisher
Copernicus GmbH
Topic
- Climate Research
Status
Published
ISBN/ISSN/Other
- ISSN: 1867-1381