Choosing an optimal <i>β</i> factor for relaxed eddy accumulation applications across vegetated and non-vegetated surfaces
<p>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 ed...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2021-09-01
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| Series: | Biogeosciences |
| Online Access: | https://bg.copernicus.org/articles/18/5097/2021/bg-18-5097-2021.pdf |
| _version_ | 1818682817168539648 |
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| author | T. Vogl T. Vogl A. Hrdina C. K. Thomas |
| author_facet | T. Vogl T. Vogl A. Hrdina C. K. Thomas |
| author_sort | T. Vogl |
| collection | DOAJ |
| description | <p>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 <span class="inline-formula"><i>β</i></span> 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.</p>
<p>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 <span class="inline-formula"><i>β</i></span> 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 <span class="inline-formula"><i>β</i></span> factor: the first two methods, referred to as model 1 and model 2, derive <span class="inline-formula"><i>β</i><sub>p</sub></span> based on a proxy <span class="inline-formula"><i>p</i></span> for which high-frequency observations are available (sensible heat <span class="inline-formula"><i>T</i><sub>s</sub></span>). 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 <span class="cit" id="xref_text.1"><a href="#bib1.bibx5">Baker et al.</a> (<a href="#bib1.bibx5">1992</a>)</span>, which computes <span class="inline-formula"><i>β</i><sub>w</sub></span> solely based upon the vertical wind statistics. The fourth method, model 4, uses a constant <span class="inline-formula"><i>β</i><sub>p, const</sub></span> derived from long-term averaging of the proxy-based <span class="inline-formula"><i>β</i><sub>p</sub></span> factor. Each <span class="inline-formula"><i>β</i></span> 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.</p>
<p>With respect to overall REA performance, we found that the <span class="inline-formula"><i>β</i><sub>w</sub></span> and constant <span class="inline-formula"><i>β</i><sub>p, const</sub></span> performed more robustly than the dynamic proxy-dependent approaches. The latter models still performed well when scalar similarity between the proxy (here <span class="inline-formula"><i>T</i><sub>s</sub></span>) and the scalar of interest (here water vapor) showed strong statistical correlation, i.e., during periods when the distribution and temporal behavior of sources and sinks were similar.
Concerning the sensitivity of the different <span class="inline-formula"><i>β</i></span> factors to atmospheric stability, we observed that <span class="inline-formula"><i>β</i><sub>T</sub></span> slightly increased with increasing stability parameter <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M18" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>z</mi><mo>/</mo><mi>L</mi></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="21pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="afe890e3a7b89417de3dc6cae75f3814"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-18-5097-2021-ie00001.svg" width="21pt" height="14pt" src="bg-18-5097-2021-ie00001.png"/></svg:svg></span></span> when no deadband is applied, but this trend vanished with increasing deadband size. <span class="inline-formula"><i>β</i><sub>w</sub></span> was unrelated to dynamic stability and displayed a generally low variability across all sites, suggesting that <span class="inline-formula"><i>β</i><sub>w</sub></span> can be considered a site-independent constant. To explain why the <span class="inline-formula"><i>β</i><sub>w</sub></span> approach seems to be insensitive towards changes in atmospheric stability, we separated the contribution of <span class="inline-formula"><i>w</i><sup>′</sup></span> kurtosis to the flux uncertainty.</p>
<p>For REA applications without deeper site-specific knowledge of the turbulent transport and degree of scalar similarity, we recommend using either the <span class="inline-formula"><i>β</i><sub>p, const</sub></span> or <span class="inline-formula"><i>β</i><sub>w</sub></span> models when the uncertainty of the REA flux quantification is not limited by the detection limit of the instrument. For conditions when REA sampling differences are close to the instrument's detection limit, the <span class="inline-formula"><i>β</i><sub>p</sub></span> models using a hyperbolic deadband are the recommended choice.</p> |
| first_indexed | 2024-12-17T10:24:52Z |
| format | Article |
| id | doaj.art-788e37dec0a8482282ce5963470d32e6 |
| institution | Directory Open Access Journal |
| issn | 1726-4170 1726-4189 |
| language | English |
| last_indexed | 2024-12-17T10:24:52Z |
| publishDate | 2021-09-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Biogeosciences |
| spelling | doaj.art-788e37dec0a8482282ce5963470d32e62022-12-21T21:52:41ZengCopernicus PublicationsBiogeosciences1726-41701726-41892021-09-01185097511510.5194/bg-18-5097-2021Choosing an optimal <i>β</i> factor for relaxed eddy accumulation applications across vegetated and non-vegetated surfacesT. Vogl0T. Vogl1A. Hrdina2C. K. Thomas3Institute for Meteorology, University of Leipzig, 04103 Leipzig, GermanyDepartment of Micrometeorology, University of Bayreuth, 95440 Bayreuth, GermanyDepartment of Civil and Environmental Engineering, Massachusetts Institute of Technology, Cambridge, MA, USADepartment of Micrometeorology, University of Bayreuth, 95440 Bayreuth, Germany<p>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 <span class="inline-formula"><i>β</i></span> 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.</p> <p>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 <span class="inline-formula"><i>β</i></span> 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 <span class="inline-formula"><i>β</i></span> factor: the first two methods, referred to as model 1 and model 2, derive <span class="inline-formula"><i>β</i><sub>p</sub></span> based on a proxy <span class="inline-formula"><i>p</i></span> for which high-frequency observations are available (sensible heat <span class="inline-formula"><i>T</i><sub>s</sub></span>). 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 <span class="cit" id="xref_text.1"><a href="#bib1.bibx5">Baker et al.</a> (<a href="#bib1.bibx5">1992</a>)</span>, which computes <span class="inline-formula"><i>β</i><sub>w</sub></span> solely based upon the vertical wind statistics. The fourth method, model 4, uses a constant <span class="inline-formula"><i>β</i><sub>p, const</sub></span> derived from long-term averaging of the proxy-based <span class="inline-formula"><i>β</i><sub>p</sub></span> factor. Each <span class="inline-formula"><i>β</i></span> 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.</p> <p>With respect to overall REA performance, we found that the <span class="inline-formula"><i>β</i><sub>w</sub></span> and constant <span class="inline-formula"><i>β</i><sub>p, const</sub></span> performed more robustly than the dynamic proxy-dependent approaches. The latter models still performed well when scalar similarity between the proxy (here <span class="inline-formula"><i>T</i><sub>s</sub></span>) and the scalar of interest (here water vapor) showed strong statistical correlation, i.e., during periods when the distribution and temporal behavior of sources and sinks were similar. Concerning the sensitivity of the different <span class="inline-formula"><i>β</i></span> factors to atmospheric stability, we observed that <span class="inline-formula"><i>β</i><sub>T</sub></span> slightly increased with increasing stability parameter <span class="inline-formula"><math xmlns="http://www.w3.org/1998/Math/MathML" id="M18" display="inline" overflow="scroll" dspmath="mathml"><mrow><mi>z</mi><mo>/</mo><mi>L</mi></mrow></math><span><svg:svg xmlns:svg="http://www.w3.org/2000/svg" width="21pt" height="14pt" class="svg-formula" dspmath="mathimg" md5hash="afe890e3a7b89417de3dc6cae75f3814"><svg:image xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="bg-18-5097-2021-ie00001.svg" width="21pt" height="14pt" src="bg-18-5097-2021-ie00001.png"/></svg:svg></span></span> when no deadband is applied, but this trend vanished with increasing deadband size. <span class="inline-formula"><i>β</i><sub>w</sub></span> was unrelated to dynamic stability and displayed a generally low variability across all sites, suggesting that <span class="inline-formula"><i>β</i><sub>w</sub></span> can be considered a site-independent constant. To explain why the <span class="inline-formula"><i>β</i><sub>w</sub></span> approach seems to be insensitive towards changes in atmospheric stability, we separated the contribution of <span class="inline-formula"><i>w</i><sup>′</sup></span> kurtosis to the flux uncertainty.</p> <p>For REA applications without deeper site-specific knowledge of the turbulent transport and degree of scalar similarity, we recommend using either the <span class="inline-formula"><i>β</i><sub>p, const</sub></span> or <span class="inline-formula"><i>β</i><sub>w</sub></span> models when the uncertainty of the REA flux quantification is not limited by the detection limit of the instrument. For conditions when REA sampling differences are close to the instrument's detection limit, the <span class="inline-formula"><i>β</i><sub>p</sub></span> models using a hyperbolic deadband are the recommended choice.</p>https://bg.copernicus.org/articles/18/5097/2021/bg-18-5097-2021.pdf |
| spellingShingle | T. Vogl T. Vogl A. Hrdina C. K. Thomas Choosing an optimal <i>β</i> factor for relaxed eddy accumulation applications across vegetated and non-vegetated surfaces Biogeosciences |
| title | Choosing an optimal <i>β</i> factor for relaxed eddy accumulation applications across vegetated and non-vegetated surfaces |
| title_full | Choosing an optimal <i>β</i> factor for relaxed eddy accumulation applications across vegetated and non-vegetated surfaces |
| title_fullStr | Choosing an optimal <i>β</i> factor for relaxed eddy accumulation applications across vegetated and non-vegetated surfaces |
| title_full_unstemmed | Choosing an optimal <i>β</i> factor for relaxed eddy accumulation applications across vegetated and non-vegetated surfaces |
| title_short | Choosing an optimal <i>β</i> factor for relaxed eddy accumulation applications across vegetated and non-vegetated surfaces |
| title_sort | choosing an optimal i β i factor for relaxed eddy accumulation applications across vegetated and non vegetated surfaces |
| url | https://bg.copernicus.org/articles/18/5097/2021/bg-18-5097-2021.pdf |
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