A Relaxed Eddy Accumulation (REA) LOPAP-System for Flux Measurements of Nitrous Acid (HONO)

In the present study a Relaxed Eddy Accumulation (REA) system for the quantification of vertical fluxes of nitrous acid (HONO) was developed and tested. The system is based on a three-channel-LOPAP instrument, for which two channels are used for the updrafts and downdrafts, respectively, and a third one for the correction of chemical interferences. The instrument is coupled to a REA gas inlet, for which an ultrasonic anemometer controls two fast magnetic valves to probe the two channels of the LOPAP instrument depending on the vertical wind direction. A software (PyREA) was developed, which controls the valves and measurement cycles, which regularly alternates between REA-, zero- and parallel ambient measurements. In addition, the assignment of the updrafts and downdrafts to the physical LOPAP channels is periodically alternated, to correct for differences in the interferences of the different air masses. During the study, only small differences of the interferences were identified for the updrafts and downdrafts excluding significant errors when using only one interference channel. In laboratory experiments, high precision of the two channels and the independence of the dilution corrected HONO concentrations on the length of the valve switching periods were demonstrated. A field campaign was performed in order to test the new REA-LOPAP system at the TROPOS monitoring station in Melpitz, Germany. HONO fluxes in the range of −4·1013 molecules m−2 s−1 (deposition) to +1.0·1014 molecules m−2 s−1 (emission) were obtained. A typical diurnal variation of the HONO fluxes was observed with low, partly negative fluxes during night-time and higher positive fluxes around noon. After an intensive rain period the positive HONO emissions during daytime were continuously increasing, which was explained by the drying of the upper most ground surfaces. Similar to other campaigns, the highest correlation of the HONO flux was observed with the product of the NO2 photolysis frequency and the NO2 concentration (J(NO2)·[NO2]), which implies a HONO formation by photosensitized conversion of NO2 on organic surfaces, like e.g. humic acids. Other postulated HONO formation mechanisms are also discussed, but are ranked being of minor importance for the present field campaign.

Choosing an optimal β factor for relaxed eddy accumulation applications across vegetated and non-vegetated surfaces

<p>Understanding the source and transport behavior of atmospheric trace gases is important to better quantify, predict, and mitigate anthropogenic effects on the environment and climate. The relaxed eddy accumulation (REA) method enables measuring the fluxes of atmospheric compounds for which fast-response sensors are not available. In REA applications, air is sampled depending on the direction of the vertical wind w, into a reservoir for updrafts, and a reservoir for downdrafts, respectively. Deadbands are used to select only certain turbulent motions during sampling to obtain the concentration difference. The <em>β</em> factor is used to scale the measured concentration difference between both reservoirs to the flux.</p> <p>In this study, we evaluated 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 <em>β</em> factor in combination with a suitable deadband. Observations with fast-response sensors were collected in three contrasting ecosystems offering stark differences in scalar transport and dynamics: a mid-latitude grassland ecosystem in Europe (Lindenberg, Germany), a loose gravel surface of the Dry Valleys of Antarctica, and a spruce forest site in the European mid-range mountains (Waldstein, Germany). REA applications were simulated using the high-frequency observations.</p>

Choosing an optimal <i>β</i> 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 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 βp based on a proxy p for which high-frequency observations are available (sensible heat Ts). 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 βw solely based upon the vertical wind statistics. The fourth method, model 4, uses a constant βp, const derived from long-term averaging of the proxy-based βp 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 overall REA performance, we found that the βw and constant βp, const performed more robustly than the dynamic proxy-dependent approaches. The latter models still performed well when scalar similarity between the proxy (here Ts) 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 β factors to atmospheric stability, we observed that βT slightly increased with increasing stability parameter z/L when no deadband is applied, but this trend vanished with increasing deadband size. βw was unrelated to dynamic stability and displayed a generally low variability across all sites, suggesting that βw can be considered a site-independent constant. To explain why the βw approach seems to be insensitive towards changes in atmospheric stability, we separated the contribution of w′ kurtosis to the flux uncertainty. For REA applications without deeper site-specific knowledge of the turbulent transport and degree of scalar similarity, we recommend using either the βp, const or βw 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 βp models using a hyperbolic deadband are the recommended choice.

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 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. This study evaluates a variety of different REA approaches with the goal of formulating universally applicable recommendations for an optimal choice of the β factor in combination with a suitable deadband. 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. 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 three different REA models for the β factor: The first method derives β0 based on a proxy for which high-frequency observations are available (sensible heat). The second method employs the approach of Baker et al. (1992), which computes βw solely based upon the vertical wind statistics. The third method uses a constant β derived from long-term averaging of the proxy-based β0 factor. Each β model was optimized with respect to deadband type and size before intercomparison. Concerning deadband form and size, we found an optimum in RMSE for linear deadbands with sizes of 0.5 and 0.9σw. These deadband widths make this method approximately equal to the use of a constant β factor. With respect to overall REA performance, we found that the βw and constant β from long-term measurements performed more robustly than the proxy-dependent approach β0. The latter model still performed well when scalar similarity between the proxy (here sensible heat) and the scalar of interest (here latent heat) show strong statistical correlation, i.e. during periods when the distribution and temporal behavior of sources and sinks were similar. With respect to sensitivity of β to atmospheric stability, we observed that β0 slightly increased with increasing stability parameter z / L when no deadband is applied, but this trend vanished with increasing deadband size. βw was independent of z / L. To explain these surprising differences, we separated the contribution of w' kurtosis to the flux uncertainty, which can be expressed by the median ratio of the REA flux compared to that from classical eddy covariance FREAFEC. Results showed a strong sensitivity to site conditions: While the kurtosis of w' seems to have no effect on the flux estimate at the grassland site, decreasing trends with increasing kurtosis can be observed for the loose gravel and forests sites and could explain the variability of FREAFEC within 10 %. For REA applications without deeper site-specific knowledge of the turbulent transport and degree of scalar similarity, we recommend using either the constant β or βw models when REA scalar fluxes are not expected to be limited by the detection limit of the instrument. For conditions close to the instrument detection limit, the β0 models using a hyperbolic deadband are the optimum choice.