An algorithm to detect non-background signals in greenhouse gas time series from European tall tower and mountain stations
<p>We present a statistical framework to identify regional signals in station-based CO<span class="inline-formula"><sub>2</sub></span> time series with minimal local influence. A curve-fitting function is first applied to the detrended time series to derive a...
| Main Authors: | , , , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2021-09-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | https://amt.copernicus.org/articles/14/6119/2021/amt-14-6119-2021.pdf |
| Summary: | <p>We present a statistical framework to identify regional signals in
station-based CO<span class="inline-formula"><sub>2</sub></span> 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 CO<span class="inline-formula"><sub>2</sub></span> 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 <span class="inline-formula">±2<i>σ</i></span> 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> |
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| ISSN: | 1867-1381 1867-8548 |