the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Options to correct local turbulent flux measurements for largescale fluxes using an approach based on largeeddy simulation
Andreas Ibrom
Luise Wanner
Frederik De Roo
Peter Brugger
Ralf Kiese
Kim Pilegaard
The eddycovariance method provides the most direct estimates for fluxes between ecosystems and the atmosphere. However, dispersive fluxes can occur in the presence of secondary circulations, which can inherently not be captured by such singletower measurements. In this study, we present options to correct local flux measurements for such largescale transport based on a nonlocal parametric model that has been developed from a set of idealized largeeddy simulations. This method is tested for three realworld sites (DKSor, DEFen, and DEGwg), representing typical conditions in the midlatitudes with different measurement heights, different terrain complexities, and different landscapescale heterogeneities. Two ways to determine the boundarylayer height, which is a necessary input variable for modelling the dispersive fluxes, are applied, which are either based on operational radio soundings and local in situ measurements for the flat sites or from backscatterintensity profiles obtained from colocated ceilometers for the two sites in complex terrain. The adjusted total fluxes are evaluated by assessing the improvement in energy balance closure and by comparing the resulting latent heat fluxes with evapotranspiration rates from nearby lysimeters. The results show that not only the accuracy of the flux estimates is improved but also the precision, which is indicated by RMSE values that are reduced by approximately 50 %. Nevertheless, it needs to be clear that this method is intended to correct for a bias in eddycovariance measurements due to the presence of largescale dispersive fluxes. Other reasons potentially causing a systematic underestimated or overestimation, such as lowpass filtering effects and missing storage terms, still need to be considered and minimized as much as possible. Moreover, additional transport induced by surface heterogeneities is not considered.
 Article
(9017 KB)  Fulltext XML
 BibTeX
 EndNote
Eddycovariance (EC) measurements provide fundamental data for the development of numerical models in meteorology, hydrology, and biogeosciences. In order to produce accurate flux estimates, a series of physically based corrections are usually applied (Aubinet et al., 2012). Most of them are undisputed and are therefore used in standardized data processing strategies (Aubinet et al., 2000; Mauder et al., 2013; Sabbatini et al., 2018). Nevertheless, researchers typically find a general systematic underestimation of the sum of the turbulent sensible and latent heat fluxes (H+λE) by 10 % to 30 %, when these are validated against the available energy at the surface, i.e. the difference between net radiation and ground heat flux at the surface (R_{n}−G_{0}) (HendricksFranssen et al., 2010; Stoy et al., 2013; Wilson et al., 2002). Several studies indicate that the majority of this systematic bias is caused by dispersive fluxes, which arise from correlation of spatial variations of Reynolds mean variables and are influenced by the heterogeneity below the scale of the spatial averaging. Dispersive momentum fluxes are typically neglected when applying a volume averaging operator to describe the turbulent flow in plant canopies (Lee, 2018). However, these dispersive fluxes can be significant in the context of the surface for the total transport above the canopy, where they are a result of secondary circulations (Mauder et al., 2020a). These circulations develop under convective conditions and are superimposed on the mean flow. They comprise distinct phenomena, which are largescale turbulent organized structures over homogeneous surfaces and thermally induced mesoscale circulations over heterogeneous surfaces (Inagaki et al., 2006; Kanda et al., 2004; Steinfeld et al., 2007). Both phenomena contribute to the transport of momentum and scalars between the surface and the atmosphere, but can inherently not be captured by singletower measurements (Etling and Brown, 1993).
A number of correction methods have been tested in the past in order to compensate for this systematic bias by distributing the surface energy balance (SEB) residual, which are either entirely or almost entirely towards the sensible heat flux (e.g. Charuchittipan et al., 2014; Ingwersen et al., 2011), according to the Bowen ratio (e.g. Mauder et al., 2013; Twine et al., 2000), or entirely to the latent heat flux (e.g. Wohlfahrt et al., 2010). However, an evaluation of these different SEB closure adjustment options remained somewhat inconclusive as to which of the methods under investigation is preferable (Mauder et al., 2018). Now, a novel method is available that is, in contrast to the previous methods, physically based and semiempirical in character, meaning it relates the dispersive fluxes (which are causing a systematic bias in singletower EC) to readily accessible meteorological variables, and the semiempirical coefficients are determined from a systematic parameter study using largeeddy simulations.
In this study, we will present a first realworld application of this new SEB closure correction method. More specifically, we will apply the method to data from three different EC sites with different site characteristics, such as canopy height, surface heterogeneity, and terrain complexity. One of these sites is a tall forest with an aerodynamic measurement height of 23 m, so the absolute magnitude of the correction can be evaluated by comparing the overall SEB closure before and after the correction. For two other sites, we will compare the resulting estimates for the latent heat flux with independently measured lysimetric evapotranspiration (ET) measurements. This will allow us to address the following two research questions:

How realistic is the partitioning approach of the SEB residual into latent and sensible heat flux fractions?

How well can the absolute magnitude of the correction be estimated?
We will now present further details about these three test sites, including their instrumentation and data processing chain in Sect. 2, followed by the results in Sect. 3. Then, we will discuss the implications of our findings, including the possibility to use this method for other sites in Sect. 4 before we summarize our conclusions in Sect. 5.
2.1 The semiempirical energy balance adjustment method
The method proposed by De Roo et al. (2018) relates the dispersive fluxes and hence the energy imbalance to the nondimensional, nonlocal scaling variables ${u}_{\ast}/{w}_{\ast}$ and $z/{z}_{i}$, where u_{∗} is the friction velocity, w_{∗} is the convective velocity scale, z is the height above ground, and z_{i} is the boundarylayer height. The resulting functional relationships are fitted to a set of largeeddy simulations (LESs), thereby representing the underlying transport processes (De Roo et al., 2018). As a result, two different correction equations are obtained, one for H and one for λE:
where the subscript “disp” stands for dispersive, representing the dispersive flux contribution that needs to be added as a correction to the measured fluxes, as indicated by the subscript “m”. Please note that this correction is only applicable to unstable stratification, i.e. when the Obukhov length L<0, because secondary circulations and the associated dispersive fluxes are restricted to these conditions. F_{1H}, F_{2H}, F_{1E}, and F_{2E} are semiempirical functions:
These constants were derived by De Roo et al. (2018) as the results of a curve fitting to their model output. Due to the limited grid resolution of the LES, which employed a grid spacing of 5 m in the horizontal and 2 m in the vertical direction, these functions cannot be expected to hold for measurement heights by z_{m} below 20 m. In this case, De Roo et al. (2018) suggest that the correction is scaled by the daily energy balance ratio EBR_{d}, analogously to the method by Mauder et al. (2013). Nevertheless, the partitioning of the residual is based on the LES parameter study of De Roo et al. (2018):
where the subscript “tot” stands for the total corrected heat flux, and the variable Res stands for the SEB residual based on independent field measurements, assuming that dispersion is the only significant cause for the SEB imbalance.
2.2 Soroe beech forest site (DKSor)
The Soroe beech forest site (DKSor) is located in the central part of the Danish island of Zealand (55.4858694^{∘} N, 11.6446444^{∘} E; 40 $\mathrm{m}\phantom{\rule{0.125em}{0ex}}\mathrm{a}.\mathrm{m}.\mathrm{s}.\mathrm{l}.$). It is surrounded by flat but heterogeneous terrain, which is characterized by a landcover mix comprising forests, agricultural area, and small settlements (Fig. 1). The beech forest around the EC tower is called “Lille Boegeskov”, which extends approximately 2.4 km in N–S direction and approximately 1.0 km in E–W direction. The forest itself is also heterogeneous, as some smaller patches inside this beech forest are covered with other species, mostly plantations of coniferous trees. Another larger beech forest, which is located to the northeast of the EC tower (Fig. 1), may also contribute to the flux footprint at times, depending on wind direction and atmospheric stability.
2.2.1 Micrometeorological measurements
The EC system is still very similar to the one developed in 1993 and operating since then at this site. A main feature is the long sampling tube that allows for keeping the IRGA (infrared gas analyzer; LI7000, LICOR, Lincoln, NE, USA) in a temperaturecontrolled hut close to the base of the 45 m tower. Since 2013, a Gill HS50 sonic anemometer is used at z_{m}=43.6 m. The net radiometer is a combination of a pyrgeometer (CG4, Kipp & Zonen, Delft, the Netherlands) and a pyranometer (CM11, Kipp & Zonen), each pointing both upwards and downwards. Contrary to the new ICOS setup, the devices are not ventilated. For details, see Table 1 in Pilegaard and Ibrom (2020). Ground heat flux was observed with two selfcalibrating heat flux plates (HFP01SC, Hukseflux Thermal Sensors BV, Delft, the Netherlands). The eddycovariance raw data were processed as described in Pilegaard and Ibrom (2020), applying the humiditydependent spectral dampening correction following Ibrom et al. (2007) but with a cospectral integration method by the total transfer function based on cospectral models from Horst (1997) to calculate the flux correction factor. The raw data were first processed with the software RCPM (http://wwwuser.gwdg.de/~aibrom/risoe/rcpm/RCPMdocumentation_main.html; last access: 14 December 2021), resulting in covariances and variances. Further flux corrections and quality control were applied by using custom made R scripts. The dataset used for this study covers a period from 1 April until 31 December 2018.
2.2.2 Determination of the boundarylayer height
For the determination of the boundarylayer height at Soroe, we applied the method by Batchvarova and Gryning (1991), which relies on friction velocity u_{∗}, sensible heat flux H, and air density measured at the DKSor station. In addition, radio sounding data for determining the morning temperature gradient in the free atmosphere were obtained from the worldwide repository hosted at the University of Wyoming (http://weather.uwyo.edu/upperair/sounding.html, last access: 7 May 2019) for the station Schleswig (station number 10 035), which is located about 170 km west of this site. Despite the distance, it is the closest permanent radio sounding station available, and it has a similar topographic setting as Soroe, so it is reasonable to assume the temperature gradient above the boundary layer being similar. Based on these data, we calculated the potential temperature difference between the heights of 1500 and 500 m for the 06:00 UTC sounding on all days of the observation period.
2.2.3 Determination of the flux footprint
The flux footprint for the DKSor site was calculated for every 30 min averaging interval for the entire observation period by using the simple twodimensional parametric model (FFP, Flux Footprint Prediction) by Kljun et al. (2015), which is implemented in the eddycovariance software TK3 (Mauder and Foken, 2015). Besides the measurement height z_{m}, this model requires 30 min data for the horizontal wind speed at the measurement height u(z_{m}), friction velocity u_{∗}, the Obukhov length L, and the standard deviation of the crosswind component σ_{v} as input variables, which were readily available from the EC system. In addition, the model requires z_{0}, which was set to a fixed value of 1.80 m, and z_{i} for every 30 min interval, which was determined according to the method by Batchvarova and Gryning (1990) as explained in Sect. 2.1.2. The resulting flux contributions from beech forest to the footprint of the DKSor EC dataset are presented in Fig. 3 in the form of a histogram. The most common flux contribution class is from 0.7 to 0.75, meaning the 70 % to 75 % of the respective 30 min flux originates from an area covered with beech forest based on a landcover map covering an area of 4×4 km^{2} centred around the tower. The median lies at a value of 0.704, meaning that in 50 % of the 30 min intervals more than 70.4 % of the measured flux consists of flux contributions from the beech forest. The average source contribution from beech forest is 75.6 %.
The DKSor dataset comprised a total of 12 469 values of 30 min each. From those, 6572 values fulfilled the criterion of ${z}_{i}/L<\mathrm{0}$ of the De Roo et al. (2018) method and the presence of all four main components of the energy balance, so the energy balance closure correction method could be applied. This dataset was then filtered using a threshold of 75 % flux contributions from beech forest according to the quality requirements by Mauder et al. (2013). The number of measured data was further reduced from 6572 to 4834, i.e. by 26 %, due to the footprint filtering.
2.3 TERENO preAlpine grassland sites (DEFen, DEGwg)
The two sites DEFen (Fendt; 47.8329^{∘} N, 11.0607^{∘} E; 595 $\mathrm{m}\phantom{\rule{0.125em}{0ex}}\mathrm{a}.\mathrm{m}.\mathrm{s}.\mathrm{l}.$) and DEGwg (Graswang; 47.5708^{∘} N, 11.0326^{∘} E; 864 $\mathrm{m}\phantom{\rule{0.125em}{0ex}}\mathrm{a}.\mathrm{m}.\mathrm{s}.\mathrm{l}.$) are located at flat valley bottoms in the TERENO (TERrestrial ENvironmental Observatories) preAlpine observatory, southern Germany. The DEFen site is surrounded by mildly complex terrain with differences in altitude on the order of 100 m, while the DEGwg site is located in an area with differences in altitude on the order of 1000 m. The valley bottoms in this region are usually grasslands, which are either partially managed as pasture or as meadow, and the slopes are often covered with forests up to the treeline. Both sites were chosen in a way such that their fetch is homogeneous and flat in all directions within a radius of 200 m, so most of the flux contributions can be assumed to originate from grassland, which is the target land use type. This has also been demonstrated through footprint calculations by Soltani et al. (2018).
2.3.1 Micrometeorological measurements
The instrumentation of the EC systems at DEFen (Fig. 4) and DEGwg (Fig. 5) is nearly identical, comprising a CSAT3 sonic anemometer (Campbell Scientific Inc., Logan, UT, USA) and a LI7500 infrared gas analyser (LICOR Inc., Lincoln, NE, USA) for measuring the sensible and latent heat fluxes. The measurement height is 3.5 $\mathrm{m}\phantom{\rule{0.125em}{0ex}}\mathrm{a}.\mathrm{g}.\mathrm{l}$. Net radiation is measured at a height of 2 $\mathrm{m}\phantom{\rule{0.125em}{0ex}}\mathrm{a}.\mathrm{g}.\mathrm{l}.$ by a fourcomponent net radiometer (CNR4, Kipp & Zonen BV, Delft, the Netherlands) and ground heat flux at the soil surface is measured by a combination of three selfcalibration heat flux plates (HFP01SC), three soil temperature profiles (T106, Campbell Scientific Inc., Logan, UT, USA), and three soil water content profiles (CS616, Campbell Scientific Inc., Logan, UT, USA) following the PlateCal method by Liebethal (2005), which is a combination of heat flux plate measurements and a calorimetric approach. Further details about the additional meteorological measurements at these sites can be found in Kiese et al. (2018).
The data processing follows the strategy for quality and uncertainty assessment of longterm EC measurements by Mauder et al. (2013). More specifically, we applied the double rotation method to align the coordinate system into the mean streamlines (Kaimal and Finnigan, 1994). We corrected for humidity fluctuations in the sensible heat flux measurement according to Schotanus et al. (1983). We compensated for spectral losses according to the method by Moore (1986) and corrected the latent heat flux for density fluctuations following Webb et al. (1980). EC data were screened for steadystate conditions and welldeveloped turbulence according to a modified version of the method by Foken and Wichura (1996). The measurement period for this study is 1 entire year from 1 January until 31 December 2014.
No u_{∗} filtering was applied because a decoupling of the canopy layer from the air above was not considered to be likely for these sites which are covered with short grass. However, the flux data are filtered using tests on welldeveloped turbulence and steadystate conditions (Foken et al., 2004; Foken and Wichura, 1996; Ruppert et al., 2006). In order to be able to compare the latent heat fluxes measured at the TERENO preAlpine grassland EC sites with the colocated lysimeters, daily sums of ET were calculated. To this end, we applied the gapfilling approach of Reichstein (2005) based on a lookup table method by using the REddyProc software (Wutzler et al., 2018) in the same way as Mauder et al. (2018) did this for an earlier comparison of SEB adjustment methods (Mauder et al., 2020a).
2.3.2 Determination of the boundarylayer height
A ceilometer of type CL51 (Vaisala Oyj, Vantaa, Finland) was deployed at each of the two TERENO preAlpine stations DEGwg and DEFen. This instrument employs a pulsed laser diode lidar (light detection and ranging) technology, where short, powerful laser pulses are sent out in a vertical or nearvertical direction. The reflection of light (backscatter) caused by aerosols, clouds, precipitation, or another obscuration is analysed. Backscatter profiles, which are averaged over 10 min, are used to determine the boundarylayer height based on the maximum gradient method CL51 (Emeis et al., 2011; Münkel et al., 2007). This method is based on the assumption of convective conditions, so the aerosols are wellmixed throughout the boundary layer while their concentration decreases sharply in the free atmosphere. The resulting values for z_{i} were used as input for the energy balance closure correction method by De Roo et al. (2018), which is only applicable for unstable conditions, meaning that during those periods also the assumptions for the boundarylayer height retrieval method can be considered to be fulfilled.
2.3.3 Lysimeter measurements of evapotranspiration
As part of the TERENOSOILCan network, the DEFen and DEGwg sites were equipped with fully automated lysimeter systems which are operated with standardized sensor systems and measuring design (Pütz et al., 2016) in close vicinity (<500 m) to the respective eddycovariance systems (see Sect. 2.2.1). At both sites, evapotranspiration was measured from weighable lysimeters (N=3) filled with intact soil cores (1 m^{2}, 1.4 m height), excavated at representative grassland locations in the surrounding of the EC stations (Kiese et al., 2018). Water fluxes at the bottom of these closed lysimeters are regulated by adjusting matrix potential in 1.4 m (TS1, METER Group, Munich, Germany) inside the lysimeter to match outside conditions measured in the same depth in the surrounding soil. If the water tension in the lysimeter is higher than outside conditions, water is pumped into a weighable tank via an underpressurized suction rake (SIC40, METER Group, Munich, Germany) and vice versa if the soil inside the lysimeter is drier than outside conditions. Grassland water fluxes of precipitation, evapotranspiration, and groundwater recharge are derived from precision weighting of each lysimeter with three load cells (model 3510, TedeaHuntleigh, Canoga Park, CA, USA, precision of 10 g, equivalent to 0.01 mm water) and water tanks in 1 min time intervals. Time series of lysimeter and water tank weights were quality checked before postprocessing by applying the adaptive window and adaptive threshold filter for separation of signal and noise (Fu et al., 2017; Peters et al., 2014). Daily evapotranspiration rates in millimetres were calculated by summing up minutebased negative weight changes of the lysimeters corrected by positive weight changes of water tanks representing lowering of lysimeter weights due to groundwater recharge.
3.1 Case study not limited by z_{m}
First, we have a look at the dataset of DKSor (Fig. 6), where the LESbased correction can be applied directly without the need to adjust the correction factor with the measured EBR_{d}, because the aerodynamic measurement height z_{m} is larger than 20 m there. The SEB closure is already relatively good on average in comparison with other sites with a slope of 0.94 and an intercept of 3.26 W m^{−2} of an orthogonal Deming regression (Manuilova et al., 2014). The scatter around this regression line can be characterized by a Pearson's correlation coefficient r of 0.915. After application of the correction, the slope increases to 0.99 and the intercept stays almost the same with a value of 3.92 W m^{−2}. Also, Pearson's r is even slightly increased with a value of 0.916. Overall, we can state that the SEB closure has clearly improved as a result of the correction with a regression line very close to identity. As expected from De Roo et al. (2018), we found that the relative contribution of dispersive fluxes to the total flux was larger for H than for λE; more specifically, H was increased by 6 % on average, and λE was increased by 4 % on average. Please note that the correction is completely independent of measurements of the available energy at the surface (R_{n}−G); the closeness of the regression line to the identity function is thus an empirical proof of the correction method. However, no alternative measurements of either ET or H are available for this beech forest site, so we can only validate the sum of the turbulent fluxes but not their partitioning between H and λE here.
In addition to this overall analysis of the energy balance closure, we also analysed the seasonal variability of the mean diurnal cycle of the modelled dispersive fluxes in comparison with the measured residual (Fig. 7). The agreement between both curves is reasonable during daytime with generally positive values of up to 50 W m^{−2}. The modelled dispersive fluxes peak already before noon local time, which is 11:00 UTC, while the measured residual peaks later in the early afternoon, at least for the months from April to September. During nighttime, the modelled dispersive fluxes are generally small and nearly zero. In contrast, the measured residual is often quite large and negative. This discrepancy reflects the fact that secondary circulations and hence also the associated dispersive fluxes are generally a phenomenon of the daytime convective boundary layer. At night, other processes obviously contribute largely to the overall SEB residual, e.g. advection or storage terms, which are not considered in the model by De Roo et al. (2018). When comparing the different seasons with each other, we find that the daytime dispersive fluxes are smaller in the months from October to December than between April and September, which can probably be explained by a combination of smaller sensible heat fluxes and less unstable conditions during this period of the year.
3.2 Case study limited by z_{m}
Next, we apply the LESbased SEB correction to the grassland datasets of DEFen and DEGwg. At both sites, the effective measurement heights were less than 20 m, so the correction had to be adjusted by the measured EBR_{d} (Eqs. 7–9). As a result, the regression line after the correction is forced to be close to identity. And indeed, the regression slope is increased from 0.77 to 1.04 for the DEFen data (Fig. 8) and from 0.70 to 1.02 for the DEGwg data (Fig. 9). For both sites, also Pearson's r increases by 0.02 as a result of the correction, which is a highly significant improvement. This is remarkable because it shows that this method also reduces the random error and not only a systematic bias. It can be seen in the original data that the closure is better at low energy fluxes, so the data are slightly bananashaped; this is nicely being corrected for.
Please note that the number of valid data points n is reduced as a result of the correction by about 6 %–7 % for both datasets, because we introduced two outlier criteria in order to avoid unrealistic fluxes when
 (a)
EBR_{d}<0.5 and EBR_{d}>1.5 and
 (b)
$\left\right({H}_{\text{res}}+\mathit{\lambda}{E}_{\text{res}}\left)\right<\mathrm{0.01}$ W m^{−2}.
The reduction in sample size is also one reason why the resulting regression slopes are slightly larger than one and not identical to one as expected after applying this correction. Another reason is the slightly negative intercept, which is caused by a few data points with a negative sum of the turbulent heat fluxes. Note that no correction was applied to these data points by this method as this is only necessary for unstable stratification.
3.3 Independent constraint on energy partitioning: water balance lysimeter
For the two grassland sites DEFen and DEGwg, the SEB closure correction needed to be scaled with the measured EBR due to the low measurement height. However, due to the nearby water balance lysimeters, we have the opportunity to test whether the partitioning of the SEB residual by this correction into the sensible and latent heat flux is realistic. To this end, we compared daily ET rates to four different options of correction for energy balance closure with these independent reference measurements; more specifically, these are the following:

No correction to the latent heat flux; that is, the entire residual is attributed to the sensible heat flux (Ingwersen et al., 2011);

Bowenratiopreserving partitioning of the SEB residual according to the daily EBR_{d} (Mauder et al., 2013);

Partitioning of the SEB residual according to the ratio between the sensible heat flux and the buoyancy flux forcing closure for every 30 min interval (Charuchittipan et al., 2014);

LESbased correction for dispersive fluxes (De Roo et al., 2018).
For DEFen, we clearly see from Fig. 10 that the first option without any correction produces systematically too low ET rates that are characterized by a regression slope of 0.70 with a moderate scatter indicated by a Pearson's r of 0.908. In contrast, the Bowenratiopreserving method leads to an overestimation of ET and a regression slope of 1.09, while the scatter is slightly reduced with Pearson's r being increased from 0.908 of 0.915, which is not a significant difference. The method by Charuchittipan et al. (2014) produces by far the largest scatter and the lowest Pearson's r of 0.628, and the slope of 1.12 is even larger than that of the Bowenratiopreserving method. Lastly, the LESbased method results in a slope of 0.9 and the highest Pearson's r of 0.937. This Pearson's r is significantly improved compared to the method by Mauder et al. (2013) at a level of p=0.08 and at a much higher level of significance compared to the other two methods.
Despite the large difference in terrain complexity between both grassland sites, the results for the DEGwg site are quite similar to those for DEFen (Fig. 11). Again, we find the largest systematic underestimation of ET if no SEB closure correction is applied with a slope of 0.65. We also find the largest scatter and the lowest Pearson's r for the method by Charuchittipan et al. (2014) with a value of 0.824 (significance level p<0.05) and the highest Pearson's r for the LESbased method with a value of 0.911, which is however not significantly improved compared to the uncorrected ET or the method by Mauder et al. (2013). The Bowenratiopreserving method is slightly overestimating with a slope of 1.02 and an intercept of 0.33 mm d^{−1}, and the LESbased method is slightly underestimating at least for larger ET values with a slope of 0.89 and an intercept of 0.23 mm d^{−1}.
Now, after this regression/correlation analysis, we will present the values for comparability and bias, which may provide additional guidance to decide which of the four tested options leads to the best agreement with lysimetric ET estimates. These results are shown in Tables 1 and 2 for DEFen and DEGwg. For both datasets, we find a large negative bias of approximately −0.35 mm if no correction is applied and a roughly equally large positive bias for the Bowenratiopreserving method. The buoyancyfluxdependent method shows smaller biases than the latter two methods but results in a poorer comparability of 1.40 and 0.855 mm for DEFen and DEGwg, respectively. Of all four methods, the LESbased correction leads to the best comparability with values of around 0.5 mm, and it also leads to the lowest biases close to zero, i.e. well below 0.1 mm d^{−1} in absolute numbers.
Closure of the SEB is to be expected for any given flux measurement site due to the first law of thermodynamics. A lack of closure indicates that not all assumptions of the EC method are sufficiently fulfilled in reality. Existing partitioning methods of the SEB residual are either based on no or a weak physical basis. The newly proposed method by De Roo et al. (2018) is based on the understanding of the relevant transport process in the unstable boundary layer. It is well known from numerical and observational boundarylayer studies that secondary circulations develop under typical daytime conditions, which are either celllike or rolllike conditions depending on the nonlocal stability parameter ${z}_{i}/L$ (Salesky et al., 2017). Both types of largescale organized structures fill almost the entire boundary layer and contribute to the overall vertical transport of scalars, such as temperature and humidity, by means of dispersive fluxes. In the presence of rolllike convection, the relative contribution is relatively small and constant over a wide stability range. As soon as celllike convection develops, the relative contribution of dispersive fluxes to the total flux increases sharply. Due to differences in typical vertical profiles of these scalars in the boundary layer, relative dispersive fluxes are larger for the transport of sensible heat than for the transport of latent heat in the surface layer (De Roo et al., 2018). No or very small dispersive fluxes are expected for nearneutral or stable stratification, because secondary circulations do not develop under those conditions (Jayaraman and Brasseur, 2021). While plausible, this LESbased method has never been validated against realworld data before.
The generally good agreement between this model for dispersive fluxes and the independent reference measurements presented above is encouraging, but is the method by De Roo et al. (2018) really the solution to the longstanding energy balance closure problem? It certainly has the soundest physical basis of all the existing SEB correction approaches, since it is based on the theoretical process understanding that the underestimation of fluxes by singletower EC systems during daytime is caused by dispersive fluxes that are generated by secondary circulations, and the semiempirical correction model is the result of a physically based and systematic LES parameter study. Indeed, its application to the DKSor dataset leads to a nearly ideal overall SEB closure. In this case, the magnitude of the dispersive fluxes was modelled directly, and there was no need to use a scaling based on measurements of the available energy at the surface. Therefore, we were able to use these independent measurements of the available energy for validating the magnitude of the predicted SEB residual.
From the comparison of mean diurnal cycles between the modelled dispersive fluxes and the measured residual, we found that the agreement is quite good during the day, meaning that the dispersive fluxes constitute indeed a major part of the missing flux, but during the night, other processes dominate, probably advection and storage terms (e.g. Moderow et al., 2009). Since sensible and latent fluxes are generally small at night, the overall energy balance can still be improved considerably through the correction for dispersive fluxes although these other terms contributing to the residual are not considered. During the summer months, strongly unstable conditions are more frequent, and these are associated with cellular convection, which is associated with large dispersive fluxes, while in autumn and winter mildly unstable conditions are dominant, which lead to the formation of rolllike secondary circulations, which are associated with smaller dispersive fluxes.
The partitioning of the residual by the method by De Roo et al. (2018) is validated by the comparison of the resulting daily ET rates with independent lysimeter measurements for the DEFen and DEGwg datasets. The SEB closure after applying this correction is not quite as ideal as for the DKSor dataset but still much improved. One reason might be that the imbalance was also initially less at DKSor; therefore, the absolute correction is higher at the two grassland sites. Nevertheless, the resulting daily ET rates after applying the LESbased correction show the best agreement, i.e. the lowest bias and the lowest RMSE, of the four different methods under investigation when compared with the lysimeter data from both sites. The agreement is very good, despite the difference in scale and methodology between EC and lysimetry. This finding shows that the partitioning by the LESbased method is reasonable, and it is preferable to all other SEB closure adjustment methods that have been published so far. In comparison to the other methods, the RMSE is approximately reduced by 50 % through the LESbased method, and the bias becomes nearly zero. Hence, this new method is clearly a step forward towards more accurate flux estimates from EC systems which are of critical importance for improving meteorological and ecological models.
However, there are also two main disadvantages of the method by De Roo et al. (2018) that should be discussed. Firstly, this method requires the application of two outlier criteria in order to avoid unrealistic fluxes (see Sect. 3.2). These use somewhat arbitrary and subjective thresholds, and they lead to a reduction in data points by 6 %–7 % for the two grassland datasets under study compared to the uncorrected data. However, this only applies to the cases that are limited by z_{m}. Theoretically, this z_{m} limitation could be overcome by higherresolution LES in the future, when this will be computationally feasible. If the correction method is not limited by z_{m}, as is the case for DKSor, no outlier criterion is needed; hence, no reduction in valid data points can be listed. Secondly, this method was developed from the results of an LES that was driven by homogeneous lower boundary conditions and therefore does not include the effects of thermally heterogeneous surface heating on dispersive fluxes, which is relevant under certain realistic conditions as discussed, for example, by Zhou et al. (2019) and Margairaz et al. (2020). Nevertheless, the correction method shows a good comparison with the respective reference measurements for the three test sites, which are far from homogeneous on the landscape scale and also represent different levels of terrain complexity. However, for even more pronounced heterogeneities, especially when they are on a scale of roughly the boundarylayer height or larger, we expect that this method is no longer valid (e.g. Eder et al., 2015).
The only site tested, where the correction method can be applied directly (DKSor), already has a relatively good SEB closure to begin with. The good closure can be explained by the rough surface in the surrounding in combination with the relatively high wind velocities that are typical for this region of Denmark. This leads to relatively high values of ${u}_{\ast}/{w}_{\ast}$, indicating forced convective conditions most of the time. In principle, an additional site with more unstable conditions would be interesting for this study as a complement. However, such sites with highquality energybalance data, which also fulfil the criterion of z_{m}>20 m are scarce. Theoretically, under more strongly unstable conditions, the LESbased correction would be much larger, and also the nonhydrostatic energy transfer might become more relevant (Sun et al., 2021). It is warranted that this correction method is further evaluated, particular for less windy sites with a sufficiently large aerodynamic measurement height, good fetch conditions, and highquality biometeorological measurements.
We demonstrated different options for how one can deal with the prerequisites for this method, which are an estimate of the boundarylayer height, and for cases that are limited by z_{m}, matching footprints between the measurement of the different energy balance components and appropriate adaptation of spectral correction methods to the respective instrumentation. The latter two conditions are identical with the prerequisites of highquality EC measurements in general and can therefore be assumed to be fulfilled after the application of a rigid quality control scheme However, the estimate of the boundarylayer height z_{i} goes beyond the standard instrumentation of longterm EC sites, although it can also be helpful for other aspects related to longterm flux measurements (Helbig et al., 2021; Wulfmeyer et al., 2020). For a site with flat terrain on the landscape and regional scale, this important nonlocal scaling variable z_{i} can be modelled from standard in situ measurements in combination with radiosounding data that are freely available worldwide. For study areas that are located in mountainous regions, such as the TERENO preAlpine sites DEFen and DEGwg, it is advantageous to use continuous remotesensing measurements of z_{i} based on ceilometers. Both methods are expected to provide an accuracy on the order of 10 % of z_{i}, which may lead to an error in the energy balance closure of the same magnitude, since it depends linearly on $z/{z}_{i}$. This correction only amounts to 5 %–30 % of the fluxes. Hence, the resulting error of the flux is less than 5 %–30 % of 10 %, i.e. 0.5 %–3 %. Moreover, the improvement in the energy balance closure, particularly in reducing the random deviations, shows that both methods work sufficiently well for this purpose.
For any operational application of such a method, it needs to be feasible, general, and accurate, and our study addresses all three of these aspects. Hence, we presented examples for the application of the novel LESbased SEB closure correction method by De Roo et al. (2018) to three longterm EC sites of different land use and different canopy structure in the midlatitudes. With respect to the accuracy of the LESbased correction method, we found that it closes the SEB almost perfectly on average for the site that is not limited by z_{m}. For the other two sites, where the application of the correction method is limited by z_{m}, the resulting bias is also close to zero when comparing the corrected latent heat fluxes with the ET estimate from nearby lysimeters. Not only the accuracy of the flux estimates is improved by this method but also the precision, which is indicated by RMSE values that are reduced by approximately 50 %. Hence, our results demonstrate that this method has the potential to be applied for operational application in longterm measurements for many sites around the world. Moreover, these results also suggest that we can simulate the relevant transport processes in the unstable boundary layer realistically with the LES. The general transferability of the idealized LES parameter study of De Roo et al. (2018) to the field has been successfully demonstrated.
However, this method is based on assumptions and has some remaining uncertainties. In its current form, it is limited to 30 min block averages for the calculation of fluxes. Moreover, this flux correction method does not account for other sources of bias or SEB nonclosure than for the atmospheric transport through dispersive fluxes caused by secondary circulations, which are restricted to unstable conditions. Therefore, it is important to note that storage terms should also be accounted for by an adequate measurement setup, if they are expected to be significant in magnitude, depending on the depth and the structure of the canopy layer. In addition, great care should be taken in the site selection, the design of the measurement setup, calibration of the instruments, the implementation of all needed flux corrections, and an effective set of quality control procedures (Mauder et al., 2021), because this SEB closure correction cannot account for any of these aspects.
The promising results of this study will hopefully encourage further validation of this LESbased method for other sites around the world, which perhaps even allow for a combined testing of magnitude and partitioning of the correction. It also remains to be evaluated at what level of surface heterogeneity this method starts to lead to unrealistic results. We expect such a failure of this method that was derived from completely homogeneous LES runs to occur when the formation of secondary circulations is dominated by surface heterogeneity rather than selforganization of turbulence. In such cases, a set of LES runs that are representative for a specific heterogeneous measurement site could potentially be used to overcome this limitation. Further investigations are also warranted to establish whether a similar SEB closure correction could also be applicable for other trace gases and scalars, e.g. CO_{2}.
The eddycovariance software TK3 is available at: https://doi.org/10.5281/zenodo.20349 (Mauder and Foken, 2015).
The evapotranspiration data of the TERENO sites Graswang and Fendt for 2013 and 2014 measured by eddycovariance and lysimeters are available at: https://doi.org/10.5281/zenodo.3957208 (Mauder et al., 2020b). The Soroe flux data are available at: http://www.europefluxdata.eu/home/sitedetails?id=DKSor (Pilegaard and Ibrom, 2019).
MM was responsible for the conceptualization, formal analysis, visualization, and writing of the original draft. AI was responsible for the conceptualization and data curation and reviewed and edited the paper with LW, FDR, PB, RK, and KP. LW visualized the project, FDR and PB developed the methodology, PB created the software, RK curated the data, and KP administered the project and collected the resources.
The contact author has declared that neither they nor their coauthors have any competing interests.
Publisher's note: Copernicus Publications remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
We thank DTU and KIT for supporting a 3month research stay by Matthias Mauder at DTU, which helped to develop the concept for this study. Lioba Martin's assistance for the development of the R script used to apply the energy balance closure correction is appreciated. We are also grateful to SvenErik Gryning for advice on how to determine boundarylayer height for the Soroe site.
Luise Wanner's contribution
was funded by the DFG as part of the CHEESEHEAD project (grant number MA
6379/11) The measurements were funded by ICOS Denmark. Funding for TERENO
and ICOSD was provided the Helmholtz Association and by BMBF. The support
by the landowners of the TERENO sites and technical staff of KIT/IMKIFU is
appreciated. This work was partially conducted within the Helmholtz Young
Investigator Group “Capturing all relevant scales of biosphereatmosphere
exchange – the enigmatic energy balance closure problem,” which was funded
by the HelmholtzAssociation through the President's Initiative and
Networking Fund and by KIT.
The article processing charges for this openaccess publication were covered by the Karlsruhe Institute of Technology (KIT).
This paper was edited by Hartwig Harder and reviewed by three anonymous referees.
Aubinet, M., Grelle, A., Ibrom, A., Rannik, U., Moncrieff, J., Foken, T., Kowalski, A. S., Martin, P. H., Berbigier, P., Bernhofer, C., Clement, R., Elbers, J., Granier, A., Grünwald, T., Morgenstern, K., Pilegaard, K., Rebmann, C., Snijders, W., Valentini, R., and Vesala, T.: Estimates of the annual net carbon and water exchange of forest: the EUROFLUX methodology, Adv. Ecol. Res., 30, 113–117, https://doi.org/10.1016/S00652504(08)600185, 2000.
Aubinet, M., Vesala, T., and Papale, D. (Eds.): Eddy Covariance – A Practical Guide to Measurement and Data Analysis, Springer, Dordrecht, 2012.
Batchvarova, E. and Gryning, S.E.: Applied Model for the Growth of the Daytime Mixed Layer, Bound.Lay. Meteorol., 56, 261–274, https://doi.org/10.1007/BF00120423, 1991.
Charuchittipan, D., Babel, W., Mauder, M., Leps, J.P., and Foken, T.: Extension of the Averaging Time in EddyCovariance Measurements and Its Effect on the Energy Balance Closure, Bound.Lay. Meteorol., 152, 303–327, https://doi.org/10.1007/s1054601499226, 2014.
Eder, F., De Roo, F., Rotenberg, E., Yakir, D., Schmid, H. P., and Mauder, M.: Secondary circulations at a solitary forest surrounded by semiarid shrubland and its impact on eddycovariance measurements, Agr. Forest Meteorol., 211–212, 115–127, https://doi.org/10.1016/j.agrformet.2015.06.001, 2015.
Emeis, S., Schäfer, K., Münkel, C., Friedl, R., and Suppan, P.: Evaluation of the Interpretation of Ceilometer Data with RASS and Radiosonde Data, Bound.Lay. Meteorol., 143, 25–35, https://doi.org/10.1007/s1054601196046, 2011.
Etling, D. and Brown, R. A.: Roll vortices in the planetary boundary layer: A review, Bound.Lay. Meteorol., 65, 215–248, https://doi.org/10.1007/BF00705527, 1993.
Foken, T. and Wichura, B.: Tools for quality assessment of surfacebased flux measurements, Agr. Forest Meteorol., 78, 83–105, 1996.
Foken, T., Göckede, M., Mauder, M., Mahrt, L., Amiro, B., and Munger, W.: Postfield data quality control, in: Handbook of Micrometeorology. A Guide for Surface Flux Measurement and Analysis, edited by Lee, X., Massman, W., and Law, B., Kluwer Academic Publishers, Dordrecht, 181–208, 2004.
Fu, J., Gasche, R., Wang, N., Lu, H., ButterbachBahl, K., and Kiese, R.: Impacts of climate and management on water balance and nitrogen leaching from three montane grassland soils in SGermany, Environ. Pollut., 229, 119–131, https://doi.org/10.1016/j.envpol.2017.05.071, 2017.
Helbig, M., Gerken, T., Beamesderfer, E., Baldocchi, D. D., Banerjee, T., Biraud, S. C., Brown, W. O. J., Brunsell, N. A., Burakowski, E. A., Burns, S. P., Butterworth, B. J., Chan, W. S., Davis, K. J., Desai, A. R., Fuentes, J. D., Hollinger, D. Y., Kljun, N., Mauder, M., Novick, K. A., Perkins, J. M., Rahn, D. A., ReySanchez, C., Santanello, J. A., Scott, R. L., Seyednasrollah, B., Stoy, P. C., Sullivan, R. C., Arellano, J. V.G. de, Wharton, S., Yi, C., and Richardson, A. D.: Integrating continuous atmospheric boundary layer and towerbased flux measurements to advance understanding of landatmosphere interactions, Agr. Forest Meteorol., 307, 108509, https://doi.org/10.1016/j.agrformet.2021.108509, 2021.
HendricksFranssen, H. J., Stöckli, R., Lehner, I., Rotenberg, E. and Seneviratne, S. I. I.: Energy balance closure of eddycovariance data: A multisite analysis for European FLUXNET stations, Agr. Forest Meteorol., 150, 1553–1567, https://doi.org/10.1016/j.agrformet.2010.08.005, 2010.
Horst, T. W.: A simple formula for attenuation of eddy fluxes measured with firstorderresponse scalar sensors, Bound.Lay. Meteorol., 82, 219–233, https://doi.org/10.1023/A:1000229130034, 1997.
Ibrom, A., Dellwik, E., Flyvbjerg, H., Jensen, N. O., and Pilegaard, K.: Strong lowpass filtering effects on water vapour flux measurements with closedpath eddy correlation systems, Agr. Forest Meteorol., 147, 140–156, 2007.
Inagaki, A., Letzel, M. O., Raasch, S., and Kanda, M.: Impact of surface heterogeneity on energy imbalance, J. Meteorol. Soc. Jpn., 84, 187–198, 2006.
Ingwersen, J., Steffens, K., Högy, P., WarrachSagi, K., Zhunusbayeva, D., Poltoradnev, M., Gäbler, R., Wizemann, H.D. D., Fangmeier, A., Wulfmeyer, V., and Streck, T.: Comparison of Noah simulations with eddy covariance and soil water measurements at a winter wheat stand, Agr. Forest Meteorol., 151, 345–355, https://doi.org/10.1016/j.agrformet.2010.11.010, 2011.
Jayaraman, B. and Brasseur, J. G.: Transition in atmospheric boundary layer turbulence structure from neutral to convective, and largescale rolls, J. Fluid Eng., 913, 1–31, https://doi.org/10.1017/jfm.2021.3, 2021.
Kaimal, J. C. C. and Finnigan, J. J.: Atmospheric Boundary Layer Flows: Their Structure and Measurement, Oxford University Press, New York, 1994.
Kanda, M., Inagaki, A., Letzel, M. O., Raasch, S., and Watanabe, T.: LES study of the energy imbalance problem with eddy covariance fluxes, Bound.Lay. Meteorol., 110, 381–404, 2004.
Kiese, R., Fersch, B., Baessler, C., Brosy, C., ButterbachBahl, K., Chwala, C., Dannenmann, M., Fu, J., Gasche, R., Grote, R., Jahn, C., Klatt, J., Kunstmann, H., Mauder, M., Rödiger, T., Smiatek, G., Soltani, M., Steinbrecher, R., Völksch, I., Werhahn, J., Wolf, B., Zeeman, M., and Schmid, H. P.: The TERENO PreAlpine Observatory: Integrating Meteorological, Hydrological, and Biogeochemical Measurements and Modeling, Vadose Zone J., 17, 180060, https://doi.org/10.2136/vzj2018.03.0060, 2018.
Kljun, N., Calanca, P., Rotach, M. W., and Schmid, H. P.: A simple twodimensional parameterisation for Flux Footprint Prediction (FFP), Geosci. Model Dev., 8, 3695–3713, https://doi.org/10.5194/gmd836952015, 2015.
Lee, X.: Fundamentals of BoundaryLayer Meteorology, Springer Atmospheric Sciences, Cham, Switzerland, 2018.
Liebethal, C., Huwe, B., and Foken, T.: Sensitivity analysis for two ground heat flux calculation approaches, Agr. Forest Meteorol., 132, 253–262, https://doi.org/10.1016/j.agrformet.2005.08.001, 2005.
Manuilova, E., Schuetzenmeister, A., and Model, F.: mcr: Method Comparison Regression, available at: https://cran.rproject.org/package=mcr (last access: 7 May 2019), 2014.
Margairaz, F., Pardyjak, E. R., and Calaf, M.: Surface thermal heterogeneities and the atmospheric boundary layer: the relevance of dispersive fluxes, Bound.Lay. Meteorol., 175, 369–395, https://doi.org/10.1007/s1054602000509w, 2020.
Mauder, M. and Foken, T.: EddyCovariance Software TK3. In Documentation and Instruction Manual of the EddyCovariance Software Package TK3 (update) (p. 67), University of Bayreuth, Zenodo [code], https://doi.org/10.5281/zenodo.20349, 2015.
Mauder, M., Cuntz, M., Drüe, C., Graf, A., Rebmann, C., Schmid, H. P., Schmidt, M., and Steinbrecher, R.: A strategy for quality and uncertainty assessment of longterm eddycovariance measurements, Agr. Forest Meteorol., 169, 122–135, https://doi.org/10.1016/j.agrformet.2012.09.006, 2013.
Mauder, M., Genzel, S., Fu, J., Kiese, R., Soltani, M., Steinbrecher, R., Kunstmann, H., Zeeman, M., Banerjee, T., De Roo, F., Kunstmann, H., and Zeeman, M.: Evaluation of energy balance closure adjustment methods by independent evapotranspiration estimates from lysimeters and hydrological simulations, Hydrol. Process., 32, 39–50, https://doi.org/10.1002/hyp.11397, 2018.
Mauder, M., Foken, T., and Cuxart, J.: Surface energy balance closure over land: a review, Bound.Lay. Meteorol., 177, 395–426, https://doi.org/10.1007/s10546020005296, 2020a.
Mauder, M., Kiese, R., and Widmoser, P.: Evapotranspiration data of the TERENO sites Graswang and Fendt for 2013 and 2014 measured by eddycovariance and lysimeters, Zenodo [data set], https://doi.org/10.5281/zenodo.3957208, 2020b.
Mauder, M., Foken, T., Aubinet, M. and Ibrom, A.: Eddycovariance measurements, in Handbook of Atmospheric Measurements, edited by: Foken, T., 1475–1506, Springer Nature Switzerland, Cham, Switzerland, 2021.
Moderow, U., Aubinet, M., Feigenwinter, C., Kolle, O., Lindroth, A., Mölder, M., Montagnani, L., Rebmann, C., and Bernhofer, C.: Available energy and energy balance closure at four coniferous forest sites across Europe, Theor. Appl. Climatol., 98, 397–412, 2009.
Moore, C. J.: Frequency response corrections for eddy correlation systems, Bound.Lay. Meteorol., 37, 17–35, https://doi.org/10.1007/BF00122754, 1986.
Münkel, C., Eresmaa, N., Räsänen, J., and Karppinen, A.: Retrieval of mixing height and dust concentration with lidar ceilometer, Bound.Lay. Meteorol., 124, 117–128, https://doi.org/10.1007/s1054600691033, 2007.
Peters, A., Nehls, T., Schonsky, H., and Wessolek, G.: Separating precipitation and evapotranspiration from noise – a new filter routine for highresolution lysimeter data, Hydrol. Earth Syst. Sci., 18, 1189–1198, https://doi.org/10.5194/hess1811892014, 2014.
Pilegaard, K. and Ibrom, A.: European Fluxes Database Cluster – Site Details DKSor, available at: http://www.europefluxdata.eu/home/sitedetails?id=DKSor, last access: 7 May 2019.
Pilegaard, K. and Ibrom, A.: Net carbon ecosystem exchange during 24 years in the Sorø Beech Forest – relations to phenology and climate, Tellus B, 72, 1–17, https://doi.org/10.1080/16000889.2020.1822063, 2020.
Pütz, T., Kiese, R., Wollschläger, U., Groh, J., Rupp, H., Zacharias, S., Priesack, E., Gerke, H. H., Gasche, R., Bens, O., Borg, E., Baessler, C., Kaiser, K., Herbrich, M., Munch, J. C., Sommer, M., Vogel, H. J., Vanderborght, J., and Vereecken, H.: TERENOSOILCan: a lysimeternetwork in Germany observing soil processes and plant diversity influenced by climate change, Environ. Earth Sci., 75, 1242, https://doi.org/10.1007/s1266501660315, 2016.
Reichstein, M., Falge, E., Baldocchi, D., Papale, D., Aubinet, M., Berbigier, P., Bernhofer, C., Buchmann, N., Gilmanov, T., Granier, A., Grunwald, T., Havrankova, K., Ilvesniemi, H., Janous, D., Knohl, A., Laurila, T., Lohila, A., Loustau, D., Matteucci, G., Meyers, T., Miglietta, F., Ourcival, J.M. M., Pumpanen, J., Rambal, S., Rotenberg, E., Sanz, M., Tenhunen, J., Seufert, G., Vaccari, F., Vesala, T., Yakir, D., and Valentini, R.: On the separation of net ecosystem exchange into assimilation and ecosystem respiration: review and improved algorithm, Glob. Chang. Biol., 11, 1424–1439, https://doi.org/10.1111/j.13652486.2005.001002.x, 2005.
De Roo, F., Zhang, S., Huq, S., and Mauder, M.: A semiempirical model of the energy balance closure in the surface layer, PLoS One, 13, e0209022, https://doi.org/10.1371/journal.pone.0209022, 2018.
Ruppert, J., Mauder, M., Thomas, C., and Lüers, J.: Innovative gapfilling strategy for annual sums of CO_{2} net ecosystem exchange, Agr. Forest Meteorol., 138, 5–18, https://doi.org/10.1016/j.agrformet.2006.03.003, 2006.
Sabbatini, S., Mammarella, I., Arriga, N., Fratini, G., Graf, A., Hörtnagl, L., Ibrom, A., Longdoz, B., Mauder, M., Merbold, L., Metzger, S., Montagnani, L., Pitacco, A., Rebmann, C., Sedlák, P., Sigut, L., Vitale, D., and Papale, D.: Eddy covariance raw data processing for CO_{2} and energy fluxes calculation at ICOS ecosystem stations, Int. Agrophysics, 32, 495–515, https://doi.org/10.1515/intag20170043, 2018.
Salesky, S. T., Chamecki, M., and BouZeid, E.: On the Nature of the Transition Between Roll and Cellular Organization in the Convective Boundary Layer, Bound.Lay. Meteorol., 163, 41–68, https://doi.org/10.1007/s1054601602203, 2017.
Schotanus, P., Nieuwstadt, F. T. M., and DeBruin, H. A. R.: Temperature measurement with a sonic anemometer and its application to heat and moisture fluctuations, Bound.Lay. Meteorol., 26, 81–93, 1983.
Soltani, M., Mauder, M., Laux, P., and Kunstmann, H.: Turbulent flux variability and energy balance closure in the TERENO prealpine observatory: a hydrometeorological data analysis, Theor. Appl. Climatol., 133, 937–956, https://doi.org/10.1007/s0070401722351, 2018.
Steinfeld, G., Letzel, M. O., Raasch, S., Kanda, M., and Inagaki, A.: Spatial representativeness of single tower measurements on the imbalance problem with eddycovariance fluxes: results of a largeeddy simulation study, Bound.Lay. Meteorol., 123, 77–98, 2007.
Stoy, P. C., Mauder, M., Foken, T., Marcolla, B., Boegh, E., Ibrom, A., Arain, M. A. A., Arneth, A., Aurela, M., Bernhofer, C., Cescatti, A., Dellwik, E., Duce, P., Gianelle, D., van Gorsel, E., Kiely, G., Knohl, A., Margolis, H., Mccaughey, H., Merbold, L., Montagnani, L., Papale, D., Reichstein, M., Saunders, M., SerranoOrtiz, P., Sottocornola, M., Spano, D., Vaccari, F., and Varlagin, A.: A datadriven analysis of energy balance closure across FLUXNET research sites: The role of landscapescale heterogeneity, Agr. Forest Meteorol., 171–172, 137–152, https://doi.org/10.1016/j.agrformet.2012.11.004, 2013.
Sun, J., Massman, W. J., Banta, R. M., and Burns, S. P.: Revisiting the Surface Energy Imbalance, J. Geophys. Res.Atmos., 126, e2020JD034219, https://doi.org/10.1029/2020jd034219, 2021.
Twine, T. E., Kustas, W. P., Norman, J. M., Cook, D. R., Houser, P. R., Meyers, T. P., Prueger, J. H., Starks, P. J., and Wesely, M. L.: Correcting eddycovariance flux underestimates over a grassland, Agr. Forest Meteorol., 103, 279–300, https://doi.org/10.1016/S01681923(00)001234, 2000.
Webb, E. K., Pearman, G. I., and Leuning, R.: Correction of the flux measurements for density effects due to heat and water vapour transfer, Q. J. Roy. Meteor. Soc., 106, 85–100, 1980.
Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier, P., Bernhofer, C., Ceulemans, R., Dolman, H., and Field, C.: Energy balance closure at FLUXNET sites, Agr. Forest Meteorol., 113, 223–243, https://doi.org/10.1016/S01681923(02)001090, 2002.
Wohlfahrt, G., Irschick, C., Thalinger, B., Hörtnagl, L., Obojes, N., Hammerle, A., Hortnagl, L., Obojes, N., and Hammerle, A.: Insights from independent evapotranspiration estimates for closing the energy balance: a grassland case study, Vadose Zone J., 9, 1025–1033, https://doi.org/10.2136/vzj2009.0158, 2010.
Wulfmeyer, V., Späth, F., Behrendt, A., Jach, L., WarrachSagi, K., Ek, M., Turner, D., Senff, C., Ferguson, C., Santanello, J., Lee, T., Buban, M., and Verhoef, A.: The GEWEX LandAtmosphere Feedback Observatory (GLAFO), GEWEX Q., 30, 6–11, 2020.
Wutzler, T., LucasMoffat, A., Migliavacca, M., Knauer, J., Sickel, K., Šigut, L., Menzer, O., and Reichstein, M.: Basic and extensible postprocessing of eddy covariance flux data with REddyProc, Biogeosciences, 15, 5015–5030, https://doi.org/10.5194/bg1550152018, 2018.
Zhou, Y., Li, D., and Li, X.: The Effects of Surface Heterogeneity Scale on the Flux Imbalance under Free Convection, J. Geophys. Res.Atmos., 124, 8424–8448, https://doi.org/10.1029/2018JD029550, 2019.