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Agricultural and Forest Meteorology 198–199 (2014) 62–71

Contents lists available at ScienceDirect

Agricultural and Forest Meteorology

j o ur na l ho me pag e: www.elsev ier .com/ locate /agr formet

ethods and uncertainties in the experimental assessment oforizontal advection

. Marcollaa,∗, I. Cobbeb, S. Minerbib, L. Montagnanib,c, A. Cescattid

Fondazione Edmund Mach, IASMA Research and Innovation Centre, Sustainable Agro-ecosystems and Bioresources Department,8010 San Michele all’Adige, Trento, ItalyRipartizione Foreste di Bolzano, Via Brennero 6, 39100 Bolzano, ItalyFree University of Bolzano, Faculty of Science and Technology, 39100 Bolzano, ItalyEuropean Commission DG Joint Research Centre, Institute for Environment and Sustainability, 21020 Ispra, Italy

r t i c l e i n f o

rticle history:eceived 8 April 2014eceived in revised form 21 July 2014ccepted 1 August 2014

eywords:O2 concentrationorizontal gradientsdvectionarbon budgetddy covariance

a b s t r a c t

The eddy covariance technique is prone to underestimating the net ecosystem CO2 exchange under sta-ble atmospheric conditions, which mostly occur at night time when other terms of the CO2 mixing ratioconservation equation (i.e. advection) may become significant. Given the potential large impact of night-time flux measurements on the estimation of the ecosystem carbon budget, it is important to developreliable methodologies for the assessment of advective fluxes. Accurate CO2 concentration gradient mea-surements are needed for a precise estimation of the advection components. For this purpose a novelmeasurement system, designed to overcome some of the key issues of CO2 concentration measurements,has been developed and deployed at the IT-Ren eddy covariance site (Renon, BZ, Italy). In particular, thesystem is optimized for minimizing both the spatial and temporal uncertainty of the CO2 concentrationgradients by adopting buffer volumes, a ramified sampling scheme, and a rapid switch between lines.

Different configurations of the measurement system were used to quantify the uncertainty of horizontalconcentration gradients and to disentangle its sources. The discrete temporal sampling was the majorsource of uncertainty, accounting for 54% of the total uncertainty, while spatial sampling accounts for39% of the total uncertainty in stable conditions and for 35% in unstable conditions, with the remaininguncertainty being explained by the accuracy of the instrumental set-up (analyser, pumps and valves)(7% and 11% for stable and unstable atmospheric conditions, respectively, corresponding to ∼0.2 ppm).

Finally, we investigated the effect of buffer volumes on the uncertainty generated by a discrete temporalsampling in the estimation of horizontal advection. The use of volumes with a mean residence timeequal to the turn-over time of the manifold reduced by half the standard deviation in the time series ofhorizontal advection (from 3.8 to 1.8 �mol m−2 s−1). The measurement system in its final configurationis currently used to quantify storage and advective fluxes at the site.

© 2014 Elsevier B.V. All rights reserved.

. Introduction

The eddy covariance technique is the most widely used methodor estimating the CO2 fluxes between the land surface and thetmosphere (Aubinet et al., 2000; Baldocchi, 2003; Baldocchit al., 2001; Moncrieff et al., 1997). However, measurementsbtained from this technique can be considered representative of

he net ecosystem exchange (NEE) only if the turbulence in thetmospheric roughness sub-layer is fully developed (Heinescht al., 2007). This requirement is typically not satisfied under

∗ Corresponding author. Tel.: +39 0461 615870.E-mail address: barbara.marcolla@fmach.it (B. Marcolla).

ttp://dx.doi.org/10.1016/j.agrformet.2014.08.002168-1923/© 2014 Elsevier B.V. All rights reserved.

certain atmospheric conditions, and especially during stable nightswhen ecosystem fluxes might be systematically underestimated(Aubinet, 2008; Finnigan, 2008; Goulden et al., 1996) and otherterms of the conservation equation need to be taken into account.In addition, the application of the technique in heterogeneous siteswith complex topography may not fulfil all its theoretical require-ments (Aubinet, 2008; Finnigan, 2004; Foken and Wichura, 1996).

The underestimation of night-time respiratory fluxes was con-firmed at several sites where turbulent fluxes measured by the eddycovariance technique were compared with independent chamber

measurements (Goulden et al., 1996; Van Gorsel et al., 2007). Thislimitation is generally overcome by rejecting data collected dur-ing critical periods, according to the theoretical requirements ofthe eddy covariance technique (Foken and Wichura, 1996), and

orest M

fir(t(Hlpn(th

taqeibHYvF(mflae

f(wwt(cmdsmcte

wmtaammsrawi2

wreltsta

B. Marcolla et al. / Agricultural and F

lling the resultant gaps with statistical methods like look up tables,esponse functions to environmental drivers or neural networksFalge et al., 2001; Moffat et al., 2007; Papale et al., 2006). In par-icular, data filtering based on a threshold value of friction velocityu*) is commonly applied in the micrometeorological community.owever, this approach suffers of both theoretical and practical

imitations (Papale et al., 2006), besides it is not proved that therocedure identifies only critical periods and all of them. An alter-ative to the u* filtering is the method proposed by Van Gorsel et al.2007), based on the assumption that advection is small relative tohe vertical turbulent flux and change in storage of CO2 in the fewours after sundown.

The measurement of additional terms of the conservation equa-ion with complex, three-dimensional experimental designs is anlternative to the filtering methods. One of the first attempts touantify the contribution of advection to the ecosystem carbonxchange was performed by Lee (1998). Afterwards several stud-es dealt with the measurements of advective terms of the carbonalance equation (Aubinet et al., 2003; Feigenwinter et al., 2004;einesch et al., 2007; Kutsch et al., 2008; Marcolla et al., 2005;i et al., 2008). Some of them reported horizontal advection andertical advection to be of opposite sign (Aubinet et al., 2003;eigenwinter et al., 2004), while others found both to be positiveMarcolla et al., 2005). Despite the different design of the instru-

ental set-up and the different methodologies to derive advectiveuxes, all these papers came to the common conclusions that: (i)dvection increases the night-time CO2 flux and that (ii) advectionstimates are highly uncertain.

One of the most advanced advection experiment was per-ormed within the framework of the CarboEurope-IP projectEU, 6th Framework Programme) at three eddy-covariance sites,ith a complex set-up consisting of four 30 m towers equippedith instrumentation for the measurements of CO2 concentra-

ion, temperature and wind velocity components at four levelsFeigenwinter et al., 2008). Data were numerically interpolated toalculate the wind and concentration fields and, ultimately, to esti-ate the control volume carbon balance. One of the conclusions

rawn in this study is that, due to the large uncertainty, it does noteem possible to use directly measured advection terms for the esti-ation of half-hourly NEE. Meanwhile, advective fluxes and their

ontribution to the ecosystem carbon balance have also been inves-igated using theoretical (Belcher et al., 2008) and modelling (Katult al., 2006; Sun et al., 2006) approaches.

Advection measurements suffer of major technical limitationshich undermine the accuracy of the estimates. In particular, theain sources of uncertainty are the estimation of vertical veloci-

ies and of horizontal CO2 concentration gradients, which are oftent the limit of the instrument accuracy (Heinesch et al., 2007)nd show large spatial variability. The accurate estimate of theean vertical wind component is difficult with available instru-entation and it needs to be corrected for several effects like

tatic offsets, dynamic offsets and anemometer tilt. Coordinateotations can be used to address these problems, with differentpproaches (Lee, 1998; Paw U et al., 2000; Wilczak et al., 2001)hile, in other studies the vertical motion is inferred from hor-

zontal divergence (Montagnani et al., 2009; Vickers and Mahrt,006).

Regarding horizontal advection, measurements of horizontalind speed can be achieved with satisfactory accuracy, while

obust estimates of horizontal concentration gradients are gen-rally difficult to obtain with the required accuracy. A commonimitation of gradient measurements is the inadequate sampling of

he underlying phenomena both in space and time. When samplingeveral points with a single analyser, the synchronous observa-ion of CO2 gradients is not possible. Discrete temporal samplingnd time averaging are therefore needed, leading to a degradation

eteorology 198–199 (2014) 62–71 63

of the measurement accuracy and to a reduction of the tempo-ral resolution. Furthermore, instrumental limitation in the numberof anemometers and lines leads to a limited spatial resolution,resulting in a systematic under-sampling of the three dimensionalwind and concentration fields (Aubinet et al., 2010). Heineschet al. (2007) investigated the effect of temporal sampling resolu-tion on the measurement of CO2 concentration time series andestimated the consequent uncertainty in the calculation of storageand advection terms. They concluded that uncertainty reduces withincreasing temporal resolution of concentration sampling. How-ever, for profile systems based on a single analyser, a limitation onthe temporal resolution is set by the need of an adequate spatialsampling. A different instrumental setup was proposed by Siebickeet al. (2011) who used a multi-analyser set-up, one for each sampledpoint, enabling simultaneous measurements of all points at highfrequency. They demonstrated that advection estimates retrievedfrom their set-up had smaller absolute values, less scattering anda good agreement with flux measurements from soil and plantchambers. The issue of spatial resolution and, in particular, the useof alternative methods to the commonly used point sampling hasnot yet been fully investigated. On this issue, Leuning et al. (2008)presented a sampling system based on perforated tubes at severalheights to retrieve horizontally averaged CO2 concentrations.

The objective of the present study is to explore the methodol-ogy and related uncertainties in the measurements of horizontalconcentration gradients and advective fluxes with a novel profilesystem specifically designed for this purpose. Different configura-tions of the measurement set-up are used to separate the effect ofinstrument precision and of temporal and spatial sampling on theuncertainty of the horizontal CO2 concentration gradient. Finally,the operational configuration of the measurement system has beentested with and without buffer volumes which act as low pass fil-ters, to evaluate the effect of the discrete temporal sampling on theaccuracy and repeatability of horizontal advection estimates.

2. Materials and methods

2.1. Site description

Measurements were taken at the intensive study site of Renon-Selva Verde (Italian Alps, 46◦59′N, 11◦43′E, 1730 m asl, mean annualtemperature 4.1 ◦C, total annual precipitation 1010 mm) whereecosystem energy, water and carbon fluxes are continuously mon-itored within the framework of the FLUXNET network (Baldocchiet al., 2001).

The Renon site is characterised by an uneven-aged mixed forestdominated by Picea abies (L.) Karst (85%), Pinus cembra L. (12%) andLarix decidua Mill. (3%), with an average density of 373 stems perhectare (dbh > 12 cm) and a LAI of 5.1. The site has a homogeneousvegetated fetch of 400 m in the main daytime wind direction (SW),while a pasture is located 60 m uphill of the eddy tower (42 m high)in the main night-time wind direction; in the East–West directionthe fetch is more than 1 km. The site topography is characterizedby a mean slope of about 11◦ North–South oriented. The canopy ishorizontally heterogeneous with large gaps between tree clusterstypical of sub-alpine forest ecosystems. A detailed description ofthe canopy structure and of the site topography as retrieved byairborne LiDAR is reported in Fig. 1 and in Cescatti and Marcolla(2004).

The tower is equipped for the measurement of standard mete-orological variables: short- and long-wave radiation components

(Kipp & Zonen CNR4, Delft, The Netherlands), photosynthetic activeradiation (Delta-T BF3, Cambridge, UK; LiCOR 190 SA, Lincoln, USA),air humidity and temperature (Rotronic MP103A, Crawley, UK) andof momentum, heat and carbon dioxide fluxes by means of the

64 B. Marcolla et al. / Agricultural and Forest Meteorology 198–199 (2014) 62–71

686350 686400 686450 686500

East (m)

5162100

5162150

5162200

5162250

1730

1740

0 4 8 12 16 20 24 28 32 36

1680

17001700

1720

0271

1740

1740

1760

1780

1800

1820

686000 686200 686400 686600 686800

East (m)

5161800

5162000

5162200

5162400

5162600

Nor

th (m

)

1630 1680 1720 1760 1800 1840 1880

Tree height (m)

ba

Elevation (m)

F (a) ano

e(Gao

2

7geetoes

llttlwatpbrmlaIb3

surement, and smoothing out short-term variations of CO2 mixingratio (Griffith et al., 2008; Leuning et al., 1999). A well-mixed vol-ume V (m3) with an initial concentration c0 will respond to an

pump

man

ifold

IRGAother

lines

1 121110 98765432 16151413(a)

(b)

(c)

P

X

B

+

P XBP XBP XB

+

ig. 1. Maps of the Renon site as retrieved from airborne LiDAR: site topography

verlaid.

ddy-covariance technique, according to the Euroflux methodologyAubinet et al., 2000). For this purpose, a 3D ultrasonic anemometerill HS (Lymington, UK) and two infrared gas analyser LiCor 7500nd LiCOR 7200 (Lincoln, Nebraska, USA) are installed at a heightf 32 m at the end of a 2 m horizontal pole pointing North–West.

.2. Advection experiment: Measurement system

The profile system is characterized by a single analyzer (LiCOR000) and 14 lines that can be configured for sampling both at sin-le points and at multiple points (for a max of 16 inlets per line),ach connected to the same suction line with a ramified tube ofqual length (Fig. 2a). The use of a single analyser is fundamen-al to minimize the instrumental uncertainty in the measurementsf gradients, as recommended by Heinesch et al. (2007). Meyerst al. (1996) showed that the error introduced by such a samplingtrategy is random and not systematic.

To limit the problem of leakages in the profile system (e.g. at theevel of the manifold, flow-meters, connectors, filters, etc.) eachine was equipped with an independent pump located betweenhe sampling line and the manifold (Fig. 2c). In this way the sys-em operates above ambient pressure and therefore any eventualeak does not affect the measurements since the leakage flow

ould be directed outward. In addition, by placing the analysert the end of the line, the pressure in the cell is independent onhe pressure in the lines since the cell outlet is open to ambientressure. This design minimizes the pressure fluctuations inducedy the pumps and the difference in pressure between lines, thuseducing one of the sources of uncertainty in profile measure-ents reported by Heinesch et al. (2007). Switching time between

ines was set to 15 s (5 s purging and 10 s reading) and the cycle

cross the lines is completed every 3.5 min. The flow rate is 6 l/min.n the final configuration each line was equipped with a 20 luffer volume, characterized by a mean residence time of about.5 min, therefore equal to the time requested to cycle the 14

d canopy heights (b). A plan of the control volume of the advection experiment is

lines. These volumes allow air mixing and act as a low-pass filter,providing a temporal average instead of an instantaneous mea-

Fig. 2. Schematic representation of the gas sampling system used to measure CO2

mixing ratio on the four walls of the control volume. Line branching (a), controlvolume (b), hydraulic scheme (c). The sampling strategy is repeated on all sides ofthe volume.

B. Marcolla et al. / Agricultural and Forest Meteorology 198–199 (2014) 62–71 65

−10

−5

05

1015

DOY

Tso

il [°C

], N

EE

[gC

m−2

d−1

]

170 185 200 215 230 245 260 275 290 305 320

TsoilNEE

ITSU_0.5m ITSU_1.6m ITSU_6m ITSU_4m ITU_1.6m IU A1 A2

Fig. 3. Timeline of the experiment performed at the Renon site in 2012. Periods referring to different system configurations, designed to disentangle the different sources ofu grey toi advecD Additi

it

c

w2

tdf

ssrfatpwpTe

2

crtsci(p

ts(ptmamcSwt

each height, six parallel gradients were calculated as the differencebetween the North and South lines at each position along the tran-sects and the standard deviation of the six values was calculated forevery half-hour. Standard deviations for each height were ranked

●

●

● ● ●

●●

● ●

● ●

● ●

●

●●

●

●

● ●●

●●

●

0.0

0.2

0.4

0.6

0.8 IU(a)

● ●●

● ● ● ●

●●

●●

● ● ●

●●

●

●

●● ●

●●

●

01

23

4

time, CET [h]

Sou

th−

Nor

th Δ

CO

2 S

tDev

[ μm

olm

ol−1

]

0 4 8 12 16 20

ITU(b)

ncertainty in the CO2 horizontal gradient measurements, are coloured in different

nstrumental and temporal sampling uncertainty, IU: instrumental uncertainty, A1:aily NEE (dashed line) and Tsoil (solid line) are also reported for the entire period.

ncoming flow f (m3 s−1) with the following time-dependent func-ion:

(t) = c0 exp(−t

�

)(1)

ith a time constant � = V/f (Winderlich et al.,010).

The use of buffer volumes with a time constant equal to theurnover time of the system can effectively reduce the uncertaintyue to the discrete temporal sampling that is particularly criticalor a system with a large number of lines (Yi et al., 2008).

In the operational configuration, optimized for long term mea-urements of horizontal advection, the horizontal gradients wereampled at three different heights (0.5, 1.6 and 7.8 m, which cor-espond to 2%, 6.3% and 30% of mean canopy height), along theour walls of a 45 m × 45 m control area (Figs. 1 and 2b). The linest the highest level were ramified to 4 sampling points, while athe two lowest levels were ramified to 16 equally spaced samplingoints along each side of the control volume (Fig. 2b). Ramificationas designed to maintain an equal tube length between samplingoints and to equally distribute the flow between all the branches.wo additional lines were installed at 16 and 32 m height on theddy tower.

.3. Uncertainty analysis of the horizontal gradient

Estimates of horizontal advection are based on horizontal con-entration gradients that are often rather low and affected by aelevant uncertainty. The measurement system described in Sec-ion 2.2 was used in three different configurations to quantify andeparate the sources of uncertainty in the measurement of the CO2oncentration gradients. The time line of the experiment is reportedn Fig. 3 together with the average daily values of soil temperatureat 0.1 m depth) and net ecosystem exchange for the investigatederiod.

IU configuration (Instrumental Uncertainty, DOY 253–257):hree lines at the centre of the North and South transects wereampling air from two 30 l buffer volumes in order to avoid spatialall lines are sampling the same point) and temporal variability (theresence of a large buffer volume minimizes the effect of a discreteemporal sampling). Hence, in this configuration, only the instru-

ental setup (the combination of tubing, pumps, valves, manifoldnd analyser) is determining the uncertainty in the gradient esti-ates. Half-hourly CO2 concentration gradients were calculated

ombining each line at the North transect with each line at theouth transect. Standard deviations of the 9 resulting gradientsere calculated at each time step to quantify the uncertainty due

o the instrumental setup.

nes (ITSU: instrumental, temporal and spatial uncertainty at different heights, ITU:tion setup without buffer volumes, A2: final advection setup with buffer volumes).onal details on the measurement setup are reported in Section 2.3.

ITU configuration (Instrumental and Temporal sampling Uncer-tainty, DOY 232–238): the inlets of six lines were grouped togetherat 1.6 m at the centre of both the North and South transects. Nobuffer volumes were used in this configuration. With this set-upgradient measurements were affected both by the instrumentaluncertainty and by the uncertainty due to the discrete tempo-ral sampling. Spatial uncertainty is not included since the lineinlets were co-located and sampled independently the same point.Gradients were calculated as difference between all possible com-binations of North and South lines. Half hourly standard deviationsof the resulting 36 gradients were eventually calculated.

ITSU configuration (Instrumental, Temporal and Spatial Uncer-tainty, ITSU, DOY 173–232): six lines were installed with inletsequally spaced along the North and South transects. Measurementheight was subsequently changed from 0.4 to 1.6, 4 and 6 m. For

Fig. 4. Mean daily trends of the standard deviation (used as an uncertainty measure)of the South–North CO2 concentration gradient. Panel (a) refers to the uncertaintydue to the instrumental system (IU); panel (b) reports the uncertainty due to theinstrumental system and to the temporal sampling (ITU).

66 B. Marcolla et al. / Agricultural and Forest Meteorology 198–199 (2014) 62–71

05

1015

20

0.5 m 1.6 m

−1

01

23

4

0 9 18 27 36 45

−1

01

23

4

neutral/sta bleunstable

4 m

Sou

th−

Nor

th Δ

CO

2 [μ

mol

mol

−1]

0 9 18 27 36 45

6 m

F uth tr( class

aadsvNas

2

pst

H

widrbw

2cct9

x [m]

ig. 5. Average values of CO2 concentration gradients between the North and the So0.5, 1.6, 4 and 6 m) at different locations (0, 9, 18, 27, 36 and 45 m) for two stability

nd averaged in two stability classes (Unstable (z − zd)/L < −0.05nd Neutral Stable (z − zd)/L > −0.05 where zd is the zero planeisplacement height and L is the Obukhov length) according totability measurements at 32 m height on the tower. Using obser-ations recorded with this configuration, the dependence of theorth–South gradient uncertainty on the number of sampled pointslong the transect was analysed at the four heights for the twotability classes.

.4. Horizontal advection

Horizontal advection up to 7.8 m was computed from verticalrofiles of horizontal wind velocity and CO2 mixing ratio mea-ured with the final setting of the profiling system according tohe following equation:

adv = 1Vm

⎛⎝

7.8 m∫0

u(z)∂c(z)∂x

dz +7.8 m∫

0

v(z)∂c(z)∂y

dz

⎞⎠ (2)

here Vm is the molar volume of dry air, (u, v) represent the hor-zontal velocity components in the North–South and East–Westirection, respectively, and c is the CO2 mixing ratio. Over-barsepresent time averages, while the coordinate system is definedy the directions normal to the walls of the control volume. Eq. (2)as numerically integrated using the trapezoidal rule.

Data from an intensive anemometric campaign performed in000 at the site (Cescatti and Marcolla, 2004) were used to cal-

ulate the shape of vertical profiles of horizontal wind velocityomponents. Data were classified according to wind direction inwo classes (North sector: Dir < 90◦ or Dir > 270◦ and South sector:0◦ < Dir < 270◦) and according to global radiation in two classes

x [m]

ansects ([CO2] South–[CO2] North). Averages where calculated for different heightses (neutral/stable z/L > −0.05; unstable z/L < −0.05).

(daytime RG > 10 W m−2, nighttime RG < 10 W m−2). The averagehorizontal velocity profiles of the East–West and North–South com-ponents were calculated for each of the four resulting classes andnormalized according to the top level velocity. Since velocity pro-files were available only in correspondence of the eddy tower,we assumed that the profile shape was independent of horizon-tal position within the control volume (Leuning et al., 2008). Asfar as mixing ratio profile is concerned, the value at z = 0 was calcu-lated with a linear extrapolation of the 0.5 and 1.6 m measurements(Marcolla et al., 2005). The calculated advection in the layer 0–7.8 mis supposed to be the major contributor to total horizontal advec-tion. In fact, the ADVEX experiment confirmed that highest absoluteadvective fluxes occurred in a relatively shallow part of the lowercanopy close to the ground (Feigenwinter et al., 2008).

Two sub-periods with different settings of the profile sys-tem were separately analysed (A1: 21/09/2012–23/10/2012; A2:24/10/2012–25/11/2012). During the second period a 20 l buffervolume was applied to each line (Fig. 2C), resulting into temporalaveraged concentration measurements (Eq. (1)). Spatial averagingalong the transects was assured in both periods by the branchingof the sampling lines. Mean and standard deviations of horizontaladvection were calculated for each sub-period, together with themean and standard deviation of the variation between two consec-utive measurements, to investigate the effect of temporal averagingon the uncertainty of advection measurements.

3. Results and discussion

3.1. Uncertainty of concentration gradients

The uncertainty in the estimates of horizontal concentrationgradients has been characterized with a series of experiments

B. Marcolla et al. / Agricultural and Forest Meteorology 198–199 (2014) 62–71 67

0.3

0.2

0.1

00.

10.

20.

3 0.5 m

StDev_S = 11.21 μmol mol−1StDev_U = 7.96 μmol mol−1

neutral/stableunstable

0

1.6 m

StDev_S = 3.69 μmol mol−1StDev_U = 2.75 μmol mol−1

−30 −20 −10 0 10 20 30

0.3

0.2

0.1

00.

10.

2

4 m

StDev_S = 2.96 μmol mol−1StDev_U = 2.45 μmol mol−1

Spatial anomaly of horizontal g radient [ μmol mol−1]

Fre

quen

cy [−

]

−30 −20 −10 0 10 20 30

6 m

StDev_S = 2.15 μmol mol−1StDev_U = 1.99 μmol mol−1

F t eachg z/L < −

wToi0ce(rlawwldsv

lamtsatotttt

ig. 6. Frequency distribution of the differences between the gradient observed aradient calculated for two stability conditions (neutral/stable z/L > −0.05; unstable

here the different sources have been included incrementally.he mean daily trends of the instrumental uncertainty (IU) andf the instrumental and temporal uncertainty (ITU) are reportedn Fig. 4. For IU an average standard deviation in the gradient of.2 �mol mol−1 is observed throughout the day. As expected, nolear daily trend is observed in the IU configuration, since it isxclusively dependent on the precision of the instrumental set-upFig. 4a). When the uncertainty due to the discontinuous tempo-al sampling is introduced (ITU), a clear daily trend appears withower values (less than 1 �mol mol−1) and lower standard devi-tions during daytime and larger values (up to 2.2 �mol mol−1)ith larger standard deviations during night-time (Fig. 4b). It isorth noting that these values are about one order of magnitude

arger than the instrumental uncertainty, clearly demonstrating theramatic impact of the discrete temporal sampling on the mea-urement accuracy and the need for fast line switching and bufferolumes in profiling systems.

Considering that the system has a turnover of 3.5 min for the 14ines, an uncertainty of 2.05 �mol mol−1 during stable conditionsnd of 1.2 �mol mol−1 for unstable conditions can be approxi-ately obtained from Heinesch et al. (2007) (Fig. 5), who report

he dependency of the uncertainty of CO2 concentration mea-urements on the number of half hourly samples. These valuesre in close agreement with nighttime and daytime values ofhe gradient uncertainty (2.08 and 1.07 �mol mol−1, respectively)bserved in the present analysis. Heinesch et al. (2007) highlight

hat the impact of this uncertainty on the estimate of horizon-al advection can range between 20% and 40% in the case of awo point measurements. Hence, a transect of several points helpso reduce the uncertainty, finding a good compromise between

x location (0, 9, 18, 27, 36 and 45 m in the East–West direction) and the average0.05) at four different heights (0.5, 1.6, 4 and 6 m).

the number of points and the sampling frequency on each ofthem.

The magnitude of the spatial variability of the horizontal gra-dient at different heights is summarized in Fig. 5 that reports theaverage South–North CO2 concentration gradient at six locations(0, 9, 18, 27, 36 and 45 m along the North and South transects) asdefined with the third system configuration (cfr. 2.3, ITSU). Aver-ages were calculated for different heights (0.5, 1.6, 4 and 6 m) atdifferent locations along the transects and for two stability classes(neutral/stable z/L > −0.05; unstable z/L < −0.05).

As expected, higher gradient values were observed in neutral-stable conditions, which also show a larger variability at the lowerheights and among the different locations. No clear East–Westtrend is evident at 0.5 and 1.6 m height, while at 4 and 6 m thereseems to be a minimum of the gradient at the centre of the tran-sect. The large variability observed between the different parallelgradients highlights the large spatial uncertainty of point measure-ments of the horizontal CO2 gradient. As pointed out by Heineschet al. (2007) lower heights are more sensitive to the spatial hetero-geneity of local sources; at 0.5 m height the range in neutral stableconditions is more than 20 �mol mol−1. The average value of CO2concentration gradient is about 0.13 and 0.08 �mol mol−1 m−1 at0.5 m height in stable and unstable conditions, respectively, anddecreases to 0.03 and 0.01 �mol mol−1 m−1 at 6 m height in stableand unstable conditions.

The magnitude of the total uncertainty (ITSU) in gradient esti-

mates is summarized in Fig. 6 as the frequency distributions ofthe difference between the point gradient at each x location andthe average South–North gradient, for two stability classes andfour measurement heights. At higher heights the distributions

68 B. Marcolla et al. / Agricultural and Forest Meteorology 198–199 (2014) 62–71

Sou

th−

Nor

th Δ

CO

2 S

tDev

[μm

olm

ol−1

]

0.5 1.6 4 6 0.5 1.6 4 6

03

69

12

●

● ●

●

●

●●

●

ITU

IU

ITU

IU

neutral/sta ble unsta ble

Fig. 7. Total uncertainty (ITSU: instrumental + temporal sampling + spatial sampling) of the CO2 gradient at four heights (0.5, 1.6, 4 and 6 m, different grey tones) and fort es repu e Sout

bt(o(6ec

Cmiacba1T1tslorobtTceT(t

auafabate

Concentration gradients observed with the final settings in theNorth–South and East–West directions have been combined athalf-hourly time scale with the correspondent vertical profiles

wo stability conditions (neutral/stable z/L > −0.05; unstable z/L < −0.05). Dashed linncertainty (ITU) estimated at 1.6 m height. White dots represent the average of th

ecome more leptokurtic and the same holds for unstable condi-ions if compared to the stable ones. The largest standard deviation11.21 �mol mol−1 for an average gradient of 5.9 �mol mol−1) isbserved at 0.5 m height in stable conditions, while the lowest1.99 �mol mol−1 for an average gradient of 0.64 �mol mol−1) at

m height in unstable conditions. Higher uncertainty in the gradi-nt estimation is therefore expected at lower levels and in stableonditions.

The total uncertainty expressed as the standard deviation of theO2 concentration gradient in the ITSU configuration for the foureasurement heights and for two stability conditions is reported

n Fig. 7. ITSU has a decreasing trend at increasing height, ands expected it increases with atmospheric stability since the CO2oncentration field is more variable in space and time under sta-le conditions. A maximum value of 10.5 �mol mol−1 is observedt 0.5 m in neutral stable conditions, while a minimum value of.2 �mol mol−1 is relative to the 6 m height in unstable conditions.he standard deviation is reduced by a factor of three from 0.5 to.6 m both in neutral-stable and unstable conditions. On the basis ofhe results obtained with the IU and ITU system configuration, thetandard deviation explained by instrumental and temporal samp-ing uncertainty (ITU) at 1.6 m height accounts for 61% and 65%f the total uncertainty in neutral-stable and unstable conditions,espectively, while the instrumental uncertainty (IU) accounts fornly 7% and 11% of the total uncertainty in neutral-stable and unsta-le conditions, respectively. Average gradients show a decreasingrend at increasing height and are lower in unstable conditions.he graph highlights the low signal to noise ratio (the averageoefficient of variation is equal to 1.77) of CO2 concentration gradi-nt measurements based on single points without buffer volumes.hese results clearly support the conclusion drawn by Aubinet et al.2010) about the limits of using advection estimates to assess theotal CO2 exchanges occurring at a site.

Observations of the simultaneous gradients along the six par-llel paths (Fig. 6) were used to quantify the dependency of thencertainty on the number of sampled locations along the Northnd South transects. In Fig. 8 results are separately shown for theour measurement heights and the two stability classes used in thenalysis. As expected, the standard deviation is lower for unsta-

le conditions at all heights and always shows a decreasing trendt increasing number of sampled locations. The decreasing rate ofhe uncertainty with the number of sampling points is larger thanxpected from the theoretical trend based on the standard error of

ort the instrumental uncertainty (IU) and the instrumental and temporal samplingh–North CO2 concentration gradient for each class.

n independent samples (solid and dotted lines in Fig. 8), probablybecause the sampled locations are not randomly distributed, butequally spaced along the transect, and are not independent. Whenfour points are sampled, the standard deviation of the gradient isless than 1 �mol mol−1 at 4 and 6 m height both for unstable andneutral stable conditions. Since in the final setting 16 points aresampled at the lowest two levels (4 in the highest level) a substan-tial reduction in the total uncertainty is expected.

3.2. Horizontal advection

Fig. 8. Dependency of the total uncertainty (ITSU) of the CO2 gradient on the numberof sampling points (dots: observed, lines: theoretical) at four heights (0.5, 1.6, 4and 6 m) and for two stability conditions (unstable: black dots and dashed lines;neutral/stable: white dots and solid lines).

B. Marcolla et al. / Agricultural and Forest Meteorology 198–199 (2014) 62–71 69

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Fig. 9. Mean daily trends of soil temperature, CO2 concentration gradients (West–East (WE) and South–North (SN)) and horizontal advection with their standard deviationf hen

t

omtta2

scttt

ta

or the configuration without buffer volumes (A1) and the subsequent period (A2) whe uncertainty of the gradient measurement.

f horizontal wind velocities (see Section 2.4) to compute theean daily trend of horizontal advection shown in Fig. 9. Advec-

ion estimates are reported together with soil temperature andhe CO2 horizontal gradients at 0.5 m height in the East–Westnd North–South directions for the two investigated periods (A1:1/09/2012–23/10/2012 and A2: 24/10/2012–25/11/2012).

During both periods the gradients are typically larger along-lope than across-slope (Siebicke et al., 2011). Despite theomparable trend and magnitude of the gradients, a major reduc-ion in the scatter of the data is evident in the second period whenhe set-up included buffer volumes to reduce the uncertainty of the

emporal sampling on the 30 min average.

Horizontal advection at the site is positive the majority ofhe time, indicating a loss of CO2 from the control volume, withn average value of 1.36 �mol m−2 s−1 in the first period and of

buffer volumes were added to each line to perform a temporal average and reduce

0.68 �mol m−2 s−1 in the second period. The observed daily trendconfirms results obtained in previous studies performed at the site(Feigenwinter et al., 2008; Marcolla et al., 2005), while absolute val-ues cannot be compared mainly because measurement campaignstook place at different time periods during the year, besides, mea-surements refer to layers of different height. Night-time values areon average higher than daytime values as a result of higher con-centration gradients and occurrence of katabatic winds. Advectionreaches its maximum values after sunset, when higher gradientsare expected to occur with decreasing air temperature and theonset of stable stratification (Siebicke et al., 2012). A similar daily

pattern of horizontal advection with a peak right after sunset wasreported also by Kutsch et al. (2008). Besides mean values, eventhe standard deviations of horizontal advection were higher dur-ing nigh-time. Non zero daytime values are not unusual, since

70 B. Marcolla et al. / Agricultural and Forest M

Fig. 10. Boxplot of the horizontal advection estimates and of the difference betweentwo subsequent advection values for the configuration without buffer volumes (A1)and the subsequent period (A2) when volumes were added to each line to perform atemporal average and reduce the uncertainty of the gradient measurement. Meansae

as(

pt(irrctuol1

4

cm(etpoi

nd standard deviations are also reported together with the average NEE value forach period.

dvection can occur both in daytime and night-time, if the canopyource-sink is spatially heterogeneous or is located on a slopeFinnigan, 2008), as it is the case at the IT-Ren site.

Despite the similar sign of horizontal advection during botheriods, there are major differences in magnitude and scatter ofhe data, which are both considerably reduced during period A2Fig. 10). The reduction in mean values is attributed to the dropn daily soil temperature (from 7.5 to 1.8 ◦C) and the consequenteduction in ecosystem respiration. On the contrary, the sharpeduction in the standard deviation of advection estimates (53%)an be largely attributed to the reduction of the uncertainty of theemporal sampling obtained with the introduction of buffer vol-mes in the period A2. These results are in-line with the estimatef the fraction of uncertainty due to the discrete temporal samp-ing resulting from the experiments reported in Section 3.1 (54% at.6 m height).

. Conclusions

There is accumulating evidence that night-time advectionaused by drainage flows is the major responsible for the underesti-ation of night-time respiration in stable atmospheric conditions

Finnigan, 2008). To investigate the issue of advection fluxes, thexperimental setting for advection estimation has to be optimized

o reduce the large uncertainty of these measurements. For thisurpose, the major sources of uncertainty affecting the estimationf horizontal CO2 concentration gradient and their effects on hor-zontal advection calculation were investigated at the site IT-Ren

eteorology 198–199 (2014) 62–71

(Renon, BZ, Italy). The profile system used in the study was designedto minimize the uncertainty due to discrete temporal sampling (bymeans of buffer volumes), spatial variability of the concentration(adopting ramified lines) and instrumental accuracy (operating thesystem above ambient pressure to avoid leakages and minimizingpressure fluctuation in the analyser). Several different configura-tions of the measurement system have been adopted to quantifyand disentangle the single sources of uncertainty that affect themeasurements of horizontal CO2 concentration gradients. Resultsshow that the instrumental uncertainty is in the order of 0.2 ppm,while the uncertainty due to the sampling in time and space withclassical single point lines can be very large at the lowest levels (upto 10 ppm at 0.5 m).

The temporal discrete sampling was observed to be a majorsource accounting for up to 54% of the total uncertainty at 1.6 mheight. A discrete spatial sampling explained 39% to 35% of the totaluncertainty in neutral/stable and unstable condition, respectively,while about 10% of the total uncertainty can be ascribed to theinstrumental setup. The use of buffer volumes in the final config-uration of the profile system with ramified lines proved to deliverestimates of horizontal advection with a reduction of 53% in thestandard deviation of the measurements.

Acknowledgements

This study was supported by EU-Project GHG-Europe and by theForest Department of the Autonomous Province of Bolzano (Italy).

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