Choosing an optimal β factor for relaxed eddy accumulation applications across vegetated and non-vegetated surfaces
Accurately measuring the turbulent transport of reactive and conservative greenhouse gases, heat, and organic compounds between the surface and the atmosphere is critical for understanding trace gas exchange and its response to changes in climate and anthropogenic activities. The relaxed eddy accumu...
| Published in: | Biogeosciences |
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| Main Authors: | , , |
| Format: | Article in Journal/Newspaper |
| Language: | English |
| Published: |
Copernicus Publications
2021
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| Subjects: | |
| Online Access: | https://doi.org/10.5194/bg-18-5097-2021 https://doaj.org/article/788e37dec0a8482282ce5963470d32e6 |
| Summary: | Accurately measuring the turbulent transport of reactive and conservative greenhouse gases, heat, and organic compounds between the surface and the atmosphere is critical for understanding trace gas exchange and its response to changes in climate and anthropogenic activities. The relaxed eddy accumulation (REA) method enables measuring the land surface exchange when fast-response sensors are not available, broadening the suite of trace gases that can be investigated. The β factor scales the concentration differences to the flux, and its choice is central to successfully using REA. Deadbands are used to select only certain turbulent motions to compute the flux. This study evaluates a variety of different REA approaches with the goal of formulating recommendations applicable over a wide range of surfaces and meteorological conditions for an optimal choice of the β factor in combination with a suitable deadband. Observations were collected across three contrasting ecosystems offering stark differences in scalar transport and dynamics: a mid-latitude grassland ecosystem in Europe, a loose gravel surface of the Dry Valleys of Antarctica, and a spruce forest site in the European mid-range mountains. We tested a total of four different REA models for the β factor: the first two methods, referred to as model 1 and model 2, derive β p based on a proxy p for which high-frequency observations are available (sensible heat T s ). In the first case, a linear deadband is applied, while in the second case, we are using a hyperbolic deadband. The third method, model 3, employs the approach first published by Baker et al. ( 1992 ) , which computes β w solely based upon the vertical wind statistics. The fourth method, model 4, uses a constant β p, const derived from long-term averaging of the proxy-based β p factor. Each β model was optimized with respect to deadband size before intercomparison. To our best knowledge, this is the first study intercomparing these different approaches over a range of different sites. With respect to ... |
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