Surface energy balance closure by the eddy-covariance method above three boreal forest stands and implications for the measurement of the CO2 flux

Agricultural and Forest Meteorology 140 (2006) 322–337 www.elsevier.com/locate/agrformet

Surface energy balance closure by the eddy-covariance method above three boreal forest stands and implications for the measurement of the CO2 flux A.G. Barr a,*, K. Morgenstern b, T.A. Black b, J.H. McCaughey c, Z. Nesic b a

Climate Research Division, Environment Canada, 11 Innovation Blvd., Saskatoon, Sask., Canada S7N 3H5 b Faculty of Land and Food Systems, UBC, Vancouver, Canada c Department of Geography, Queen’s U., Kingston, Canada Received 25 November 2005; accepted 22 March 2006

Abstract Closure of the surface energy balance provides an objective criterion for evaluating eddy-covariance (EC) flux measurements. This study analyses 5 years of EC carbon dioxide, water vapor, and sensible heat flux measurements from three mature boreal forest stands in central Saskatchewan, Canada. The EC sensible and latent heat fluxes, H and lE, underestimated the surface available energy by 11% (aspen), 15% (black spruce), and 14% (jack pine). At all sites, the energy-closure fraction CF responded similarly to the friction velocity u*, atmospheric stability, and time of day. At night, CF increased from 0.3 at very low-u* to an asymptotic maximum of 0.9 at u* above 0.35 m s1. During unstable-daytime periods, CF varied linearly from 0.7 at low-u* to 1.0 at high-u*. The energy imbalance pattern was similar among sites and may be characteristic of the continental, boreal forest. EC measurements of net ecosystem exchange F NEE have no objective, diagnostic parameter that is equivalent to CF. We therefore derived an analogous F NEE ‘‘closure fraction’’ CFNEE by normalizing measured F NEE against estimates from an empirical model that was tuned to the high-u* data. CF and CFNEE responded similarly to u*, atmospheric stability, and time of day. We discuss two implications for EC flux data post-processing. The results uphold the common practice of rejecting EC measurements during low-u* periods. They also lend support to the application of energy-closure adjustments to H, lE, and F NEE. Crown Copyright # 2006 Published by Elsevier B.V. All rights reserved. Keywords: Boreal forest; Surface energy balance; Eddy-covariance; Energy balance closure; Net ecosystem exchange

1. Introduction The eddy-covariance (EC) method has become a standard tool in the study of the terrestrial carbon, water, and energy cycles (Sellers et al., 1995; Baldocchi et al., 2001). It is the only reliable method to measure the net exchange of carbon between terrestrial ecosystems and the atmosphere over time scales of hours to years. Its

* Corresponding author. E-mail address: [email protected] (A.G. Barr).

widespread use is generating new insights into the carbon dynamics of terrestrial ecosystems, including the influence of inter-annual climate variability on the carbon and water cycles (Goulden et al., 1998; Black et al., 2000; Griffis et al., 2000; Arain et al., 2002; Barr et al., 2002, 2004, in press; Morgenstern et al., 2004), the roles of management and disturbance (Amiro et al., 2006; Coursolle et al., 2006), and the partitioning of the net ecosystem exchange (F NEE) into ecosystem respiration and photosynthesis (Black et al., 2000; Barr et al., 2002; Falge et al., 2002; Griffis et al., 2003). The EC method is also providing new insights into land

0168-1923/$ – see front matter. Crown Copyright # 2006 Published by Elsevier B.V. All rights reserved. doi:10.1016/j.agrformet.2006.08.007

A.G. Barr et al. / Agricultural and Forest Meteorology 140 (2006) 322–337

surface–atmospheric boundary layer interactions (Barr and Betts, 1997), which in turn drive cloud field evolution and convective precipitation processes (Betts et al., submitted for publication). EC flux measurements are key elements in bottom-up approaches to regional flux estimation, providing essential information for ecosystem-process model development, including the identification of key biophysical processes, the quantification of model parameters, and the validation of model output at local scales (Grant et al., 1999, 2001; Amthor et al., 2001). EC studies often report measurement deficits. Deficits in F NEE are almost universally observed during calm nights (e.g., Goulden et al., 1996; Staebler and Fitzjarrald, 2004), leading to the common practice of rejecting nighttime F NEE data at low values of the friction velocity u* (Goulden et al., 1996; Black et al., 1996; Barford et al., 2001). Studies of the surface energy balance often report an imbalance between the EC sensible and latent heat fluxes, H and lE, and the surface available energy (net radiation minus surface storage) (Barr et al., 1994, 2000, 2001; Blanken et al., 1998; Aubinet et al., 2000; Twine et al., 2000; Wilson et al., 2002; Li et al., 2005). The degree of energybalance closure provides an objective check of the EC method. When systematic energy imbalances occur, they may reveal bias, not only in H and lE, but also in F NEE (Twine et al., 2000). The application of energyclosure type adjustments to F NEE (Blanken et al., 1998; Twine et al., 2000; Griffis et al., 2003; Barr et al., 2002, 2004), however, remains controversial because the connection between EC F NEE deficits and energy balance non-closure is not firmly established. The EC

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community has not agreed on a standard approach to remove flux deficits from EC time series. This study reports 5 years (2000–2004) of EC carbon, water, and energy flux measurements from three boreal forest sites in the Boreal Ecosystem Research and Monitoring Sites (BERMS) network in central Saskatchewan, Canada. All three sites are part of the Fluxnet-Canada Research Network. Special care is given to the accurate measurement of net radiation and the surface energy storage-fluxes. The objectives are: to characterize the energy-closure fraction CF by the EC method above three proximate boreal forest sites, with respect to time of day, friction velocity, and atmospheric stability; to develop an index that quantifies the EC F NEE deficit; and to explore linkages between the F NEE deficit and CF. The goal of the study is to increase our confidence in EC data post-processing, including the application of low-u* data exclusions and the use (or non-use) of energy-closure adjustments. 2. Measurements and data 2.1. Sites This study was conducted at three mature, boreal forest stands in central Saskatchewan, Canada. The sites were established in 1994 as part of the Boreal Ecosystem-Atmosphere Study (BOREAS, Sellers et al., 1997) and have continued since 1997 as part of the Boreal Ecosystem Research and Monitoring Sites (BERMS) program (http://berms.ccrp.ec.gc.ca). Table 1 summarises the salient site characteristics. Additional details are given in Blanken et al. (1997),

Table 1 Characteristics of the BERMS sites and EC measurements

Location Elevation (m) Slope (%) Dominant tree species Stand density (stems ha1) Leaf area index Canopy height (m) EC height (m) Fetch Unstable flux footprint (m)a Neutral to stable flux footprint (m)a a

Kljun et al. (in press).

SOA

SOBS

SOJP

53.269 8N, 106.2008W 600.6 1 Trembling aspen (Populus tremuloides Michx) 980 4.4 21 39 3 km in all directions

53.9878N, 105.1178W 628.9 1 Black spruce (Picea mariana (Mill.) B.S.P.) 5900 4.2 11 25 >1.5 km in all directions, most uniform to 300 m 200–300 500

53.9168N, 104.6908W 579.3 2–5 Jack pine (Pinus banksiana Lamb). 1190 2.5 14 29 >1 km in all directions

450–550 900

250–400 600

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Jarvis et al. (1997), Baldocchi et al. (1997), and Gower et al. (1997). The flux towers at all three sites have extensive uniform fetch (Griffis et al., 2003; Kljun et al., in press). A comparison of aircraft and tower flux measurements during BOREAS (Desjardins et al., 1997) confirmed that both the net radiation and the EC flux footprints are representative of the larger fetch at these sites.

2.3. Net ecosystem exchange

2.2. Surface energy balance

where Z z dc Sc ¼ ra dz dt 0

The surface energy balance may be written as: Rn ¼ H þ lE þ Qg þ Qb þ Qp

(1)

where Rn is the net radiation flux density, H the surface sensible heat flux density, lE the surface latent heat flux density, Qg the soil heat flux density, Qb the rate of change of heat storage in the above-ground biomass and Qp is the energy flux density associated with the CO2 flux (through photosynthesis and respiration). All terms in Eqs. (1)–(5) have units of (W m2). Eq. (1) pertains to the warm season only; it does not include terms for freeze–thaw events or heat storage in the snow pack, neither of which were measured in this study. The Q terms in Eq. (1) are often referred to as surface energy storage-fluxes. We will denote the sum (Qg + Qb + Qp) as Q and will refer to the difference (Rn  Q) as the surface available energy. The EC method determines the surface flux as the sum of the turbulent eddy-flux, measured above the surface, and the flux divergence between the surface and the EC measurement level, estimated from the change in storage. H and lE are computed as the sum of the EC fluxes at height z (Hz and lEz) and the EC storage-fluxes (SH and SlE), H ¼ H z þ SH

(2)

where Z z dT a dz rcp SH ¼ dt 0

(3)

and lE ¼ lEz þ SlE

(4)

where SlE ¼

Z

z

rl 0

dq dz dt

(5)

Ta is the air temperature, q the specific humidity, r the density of moist air, cp the specific heat of moist air and l is the latent heat of evaporation.

The net ecosystem exchange (F NEE) quantifies the exchange of carbon between an ecosystem and the atmosphere. F NEE is estimated as the sum of the EC flux at height z (F c) and the rate of change of CO2 storage in the air layer below height z (Sc): F NEE ¼ F c þ Sc

(6)

(7)

ra is the density of dry air and c is the CO2 mixing ratio. F NEE is positive when the atmosphere is gaining carbon from the ecosystem. In situations where the atmospheric flux is the only significant mechanism for carbon gain or loss by the ecosystem, F NEE provides a direct measurement of the net ecosystem production NEP, i.e., NEP = F NEE. The value of NEP results as the balance between carbon uptake through gross ecosystem photosynthesis P and release through ecosystem respiration R: NEP ¼ P  R

(8)

2.4. Flux measurements The net radiation flux density Rn was calculated from component measurements of upwelling and downwelling shortwave and longwave radiation, made using paired Kipp and Zonen (Delft, The Netherlands) model CM11 pyranometers and paired Eppley Laboratory (Newport, RI, USA) model PIR pyrgeometers. The longwave radiation was calculated using Eq. (11) from Fairall et al. (1998) with a value for B of 3.2. A duplicate Rn measurement, made using a Middleton (Carter-Scott Design, Brunswick, Australia) model CN1-R net radiometer, typically agreed to within 2%. The downwelling photosynthetically active photon flux density (Q#) was measured using LI-COR Inc. (Lincoln, NE, USA) model LI190sa quantum sensors. The upward-facing radiometers were mounted atop the scaffold flux tower in ventilated housings to minimize dew and frost on the sensor domes. The net radiometer and the downward-facing radiometers were mounted on a horizontal boom that extended 4 m to the south of the flux tower, 10 m above the forest canopies. All radiometers were factory calibrated in 1996 and recalibrated at Environment Canada’s National Atmospheric Radiation Centre (NARC, Downsview, Ontario)

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once before deployment and every 2–3 years thereafter. The repeated calibrations agreed to within 1% for the CM11 and 3% for the PIR. The soil heat flux Qg was measured using four Middleton (Carter-Scott Design, Brunswick, Australia) model CN3 soil heat flux plates at the 3 cm depth at SOA and 10 cm depth at SOJP and SOBS, corrected for the heat storage change in the soil layer above the flux plates. The biomass heat storage-flux Qb was estimated from 6 to 15 measurements of tree bole temperature using 30-guage chromel–constantan thermocouples at several depths in the bole (Blanken et al., 1997; McCaughey and Saxton, 1988), using the biomass estimates of Gower et al. (1997). The photosynthetic energy flux Qp was calculated from the EC measurements of F NEE (Blanken et al., 1997). Eddy-covariance measurements of H, lE, F NEE, and the friction velocity u* were made from a twin scaffold tower at twice the height of the forest canopy. The EC system consisted of a tri-axial sonic anemometer (Gill Instruments Ltd. (Lymington, UK) model R3 at SOA and SOBS and Campbell Scientific Inc. (Logan, UT, USA) model CSAT3 at SOJP) in combination with a thermostated, closedpath, infrared gas (CO2/H2O) analyzer (LI-COR Inc. (Lincoln, NE, USA) model LI 6262) operated in absolute mode. A diaphragm pump was used to draw air into the IRGA at a flow rate of 10 l min1, through a 4 m long, 4 mm inner-diameter, heated tube (Synflex 1300, Saint Gobain Performance Plastics, Wayne, NJ, USA). The data acquisition system included an Iotech Inc. (Cleveland, OH, USA) DAQbook 200 and a Pentium 166 MHz PC. Additional details are given in Black et al. (1996) and Chen et al. (1999). To ensure that the EC measurements were comparable among sites, identical instrumentation, data acquisition hardware and post-processing software were used at all three sites, except for the difference in sonic anemometers. SH and SlE were calculated using trapezoidal integration of profile measurements at three to five levels (1, 4, 18, and 36 m at SOA; 1, 6, and 25 m at SOBS; and 1, 4, 8, 14, and 28 m at SOJP) made using Vaisala HMP45cf temperature–humidity probes. Previous studies at SOA (Blanken et al., 1997; Yang et al., 1999) verified that these sampling heights estimated SH and SlE to an accuracy of 10%. Sc was calculated from measurements of the CO2 profile at eight levels made using an LI 6262 infra-red gas analyzer. Unlike SH and SlE, which were measured throughout the study, the Sc measurements began in mid 2000 at SOA, early 2003 at SOBS, and late 2003 at SOJP. At SOJP only, the Sc

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time series was too short to support the some of detailed analyses below. Atmospheric stability was estimated using the stability parameter j j¼

zd L

(9)

where d is the zero-plane displacement (estimated as 63% of the canopy height), L is the Monin–Obukov length (Garratt, 1992), L¼

ra cp #u3 kgH v

(10)

u is the potential temperature, k the von Karman’s constant, g the acceleration of gravity and Hv is the virtual heat flux density, H v ¼ H þ 0:07lE

(11)

The evaporative fraction EF was estimated as: EF ¼

lE H þ lE

(12)

2.5. Estimating annual E, FNEE, R, and P Gaps in lE were filled using moving-window linear regression, with Rn  Q as the independent variable and measured lE as the dependent variable. Daytime and nighttime periods were treated separately. We estimated the temporal variation in the regression coefficients using a flexible moving window of 240 measured (notmissing) data points, moved in increments of 48 points at a time. Standard methods were used to fill gaps in NEP and partition NEP into R and P (Griffis et al., 2003; Barr et al., 2004), with variations as described below. R was estimated as R = F NEE at night and during the cold season, when P was zero. An empirical R = f(t, T) model was then fit to the measured R data,   a1 R ¼ fR (13) 1 þ ea2 ða3 TÞ where T is temperature (either soil temperature Ts at 2 cm depth or air temperature Ta at 2/3 of the canopy height), a1 to a3 are empirical constants and the timevarying parameter f R quantifies the fractional departures of R from the mean R versus T relationship (the term inside the square brackets). Eq. (13) was used to fill gaps in R at night and during the cold season. The parameters were fit in two steps: a1 to a3 were fit using all measured (nighttime and cold-season) R and T data, with f R fixed

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at 1.0; then the temporal variation in parameter f R was fit using a flexible, moving window of 100 measured (not-missing) data points, corresponding to a period of 3–14 days. The window was moved in increments of 20 points. Daytime, warm-season R was estimated in two ways, either using Eq. (13), tuned to the nighttime data, or as the y-intercept of the daytime NEP light-response curve NEP ¼

fPx Q # ðfQ # þ Px Þ  R

(14)

where f is the ecosystem quantum yield and Px is the light-saturated asymptote for P. ‘‘Measured’’ P was then calculated from measured NEP and modeled R as R + NEP. Lastly, gaps in P were filled using an empirical P light-response curve, fit to the measured data. P¼

fPx Q # fQ # þ Px

(15)

The temporal variations in Px, f, and R were estimated from measured daytime NEP (Eq. (14)), P (Eq. (15)), and Q# using a flexible, moving window of 240 measured (not-missing) data points, moved in increments of 48 points. The moving window typically spanned 7–14 days. 2.6. Surface energy-balance closure Energy imbalances occur in EC studies when the left and right hand sides of Eq. (1) do not balance. The energy-closure fraction (CF) is defined as: CF ¼

H þ lE Rn  Q

(16)

and its eddy- and storage-flux components are defined, respectively, as CFe ¼

H z þ lEz Rn  Q

(17)

CFs ¼

SH þ SlE Rn  Q

(18)

Hz + lEz, and SH + SlE, respectively, as the dependent variables. The regression approach provided a stable and robust estimate of CF even when Rn  Q was near zero. We calculated annual values of CF and its components at each site. We also evaluated the behaviour of CF for several stratifications of the data, based on u*, j, and day versus night. The effect of u* on CF was assessed by (a) grouping the data into nighttime, neutral-and-stable-daytime (j > 0.1), and unstable-daytime (j < 0.1) periods, (b) stratifying each group based on u* percentiles into up to ten strata with at least 500 points per stratum, and (c) estimating CF and its components for each stratum. Similarly, the effect of j on CF was assessed by grouping the data into nighttime and daytime periods and then stratifying each group by j into up to ten strata with at least 500 points per stratum. 2.7. The FNEE closure fraction The EC measurement of F NEE has no objective, diagnostic parameter that is equivalent to CF. To evaluate deficits in F NEE, therefore, we developed an analogous parameter which we will call the ‘‘F NEE closure fraction’’ (CFNEE). CFNEE normalizes measured F NEE against a modeled estimate Fˆ NEE from Eqs. (13), (15), and (8), with coefficients tuned to the high-u* data only (u* > 0.35 m s1). For individual periods, CFNEE was estimated as F NEE =Fˆ NEE . For multiple periods, CFNEE was estimated by linear regression, forced through the origin, of F NEE versus Fˆ NEE . The eddy- and storage-flux components were calculated as CFNEE ¼ e ˆ NEE . Note that, unlike CF, F c =Fˆ NEE and CFNEE ¼ S = F c s CFNEE is a relative bias, with a mean that is constrained to 1.0 at u* > 0.35 m s1. Our purpose in deriving CFNEE was to characterize how the F NEE flux deficits varied with time of day, u*, and j and assess if CFNEE and CF responded similarly. Similar indices have been used by Blanken et al. (1998), Wilson et al. (2002), and Lee and Hu (2002). 3. Results and discussion

where CF = CFe + CFs. Because Rn  Q was measured independently, CF is an objective parameter that can be used to evaluate EC measurements of H and lE. We calculated CF during warm-season periods only when all of the surface storage terms were measured. CF, CFe, and CFs were estimated as simple ratios (Eq. (16)–(18)) or by linear regression, forced through the origin, with Rn  Q as the independent variable and H + lE,

3.1. The effect of friction velocity on the energyclosure fraction Table 2 shows the energy-closure fraction CF, stratified by time of day, u*, and j. CF ranged from 0.85 to 0.90 except at night when the value was lower (0.65–0.79). The exclusion of low-u* data raised the nighttime CF mean to 0.91, similar to the corresponding daytime mean of 0.88.

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Table 2 Energy-closure fractions, stratified by time of day, u* and atmospheric stability Stratification

SOA

SOBS

SOJP

All sites

All periods Nighttime only Nighttime (u* > 0.35 m s1) Daytime Daytime (u* > 0.35 m s1) Daytime (j > 0.1) Daytime (j < 0.1)

0.890  0.002 0.790  0.008 1.021  0.013 0.892  0.003 0.905  0.003 0.899  0.005 0.892  0.004

0.854  0.002 0.658  0.009 0.887  0.019 0.856  0.003 0.868  0.003 0.839  0.006 0.854  0.003

0.856  0.003 0.647  0.007 0.831  0.015 0.859  0.003 0.864  0.003 0.874  0.005 0.847  0.005

0.867  0.002 0.700  0.008 0.913  0.016 0.869  0.003 0.879  0.003 0.871  0.005 0.865  0.004

The uncertainty is estimated from the 95% confidence intervals on the estimates for CF.

Fig. 1 shows the dependence of CF on u* for nighttime, neutral-and-stable-daytime (j > 0.1), and unstable-daytime (j < 0.1) periods. The most striking features are the strong curvilinear dependence of CF on u* during nighttime and neutral-and-stable-daytime periods, the equally well-defined linear dependence of CF on u* during unstable-daytime periods, and the similarity among sites. At night (left panel), CF increased from a minimum of 0.32  0.10 at near-zero u* to an asymptotic maximum of near 1.0 at u* above 0.35 m s1. The small CF values at very low-u* were associated with near-zero values of the eddy-flux component CFe which were only partially offset by the storage-flux component CFs. The analysis shows that the well-documented failure of EC CO2 flux

measurements at low-u* at night (Goulden et al., 1996; Black et al., 1996) also affected H and lE. During unstable-daytime periods (right panel), CF increased linearly with u* from a minimum of 0.70  0.03 at u* of 0.10 m s1 to a maximum of 0.96  0.02 at u* of 0.85 m s1. The increase was due almost entirely to the eddy-flux component. During neutral-and-stabledaytime periods (center panel), CF, CFe, and CFs were intermediate, with similarities to the nighttime values at low-u* and similarities to the unstable-daytime values at high-u*. Based on Fig. 1, low-u* data exclusion largely (but not completely) resolved the energy imbalance problem at night but not during the day. The CF–u* relationships were remarkably consistent among sites, with two subtle differences. During

Fig. 1. The effect of the friction velocity u* on the energy-closure fraction CF (upper panel) and its eddy-flux (middle panel) and storage-flux (lower panel) components, for nighttime, neutral-and-stable-daytime (j > 0.1), and unstable-daytime (j < 0.1) periods.

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neutral-and-stable-daytime periods, the sites diverged at low to moderate u* because of differences in CFe. At night, the sites diverged at high-u* (>0.35 m s1), with asymptotic CF maxima that ranged from 0.83 at SOJP to 1.03 at SOA. However, the nighttime differences were small in absolute terms because the fluxes were small, well within the measurement uncertainty in Rn  Q. The consistency in the CF–u* relationship among sites contrasts with the analysis of Wilson et al. (2002), who reported large inter-site differences in energybalance closure at 22 FLUXNET sites, with no obvious attribution of the differences to site or EC system characteristics. Unlike the FLUXNET sites, the sites in this study were relatively uniform: all are continental; all have tall, forest canopies with flat, level topography and extensive, uniform fetch; all have identical, highquality measurements of surface available energy; and all use standardized EC instrumentation, data-acquisition hardware, and data-processing software. The CF– u* relationships may be characteristic of continental boreal forests with level topography or they may typify the closed-path EC systems used in this study. The similarity among sites points to the great benefits of measurement standardization. CF quantifies the energy flux deficit but does not show its partition between H and lE. Fig. 2 plots the dependence of CF on the evaporative fraction EF. At SOA, where the seasonal LAI cycle produced a broad EF range, CF had a weak yet significant dependence on EF. The linear regression relationship shows that the flux deficit was greater for lE (16%) than H (9%). At SOBS and SOJP, where the range in EF was narrower, CF and EF were uncorrelated. 3.2. The effect of friction velocity on the measurement of FNEE Fig. 3 repeats the analysis of Fig. 1 but using the F NEE ‘‘closure fraction’’ CFNEE (Section 2.7). Values of CFNEE and CFNEE are not shown for SOJP because of s the lack of Sc data. The similarities between Figs. 1 and 3 are striking. Like CF, CFNEE had a strong, curvilinear dependence on u* during nighttime and neutral-andstable-daytime periods, and a well-defined but less pronounced dependence on u* during unstable-daytime periods. Like CF, the CFNEE analysis demonstrated the failure of the EC method at nighttime u* values below 0.35 m s1, the stability of the EC method at nighttime u* values above 0.35 m s1, and the consistency among sites. The nighttime CFNEE and CFNEE dependencies on e s u* matched the idealized Fig. 4 of Massman and Lee

(2002). The u* threshold was higher than used in most previous studies (Goulden et al., 1996; Barford et al., 2001; Barr et al., 2002; but not Black et al., 2000; Barr et al., 2004). It may be typical of tall, closed-canopy forests. Blanken et al. (1998) reported similar relationships between u* and the ratios of H, lE, and F c to the corresponding ‘‘expected’’ fluxes at SOA in 1994, with similar nighttime u* thresholds. Wilson et al. (2002) evaluated the CF versus u* and F c versus u* relationships at night for two contrasting sites, a 25 m mixed conifer forest and an agricultural crop. The CF–u* and F c–u* relationships were similar for the forest but not the crop. Our analysis extends these earlier studies to include daytime periods and separates the relative contributions of the eddy-fluxes and storage-fluxes. It also shows the similar behaviour of the EC flux deficits among proximate sites with comparable aerodynamic roughnesses and matched EC systems. Analysis of CF and CFNEE values from individual 30 min periods showed that u* was more effective than j in identifying severe flux deficit periods and nearly as effective as u* and j combined (Aubinet et al., 2000). This result contrasts with Staebler and Fitzjarrald (2004) who found that u* alone was only partially effective in eliminating nighttime F NEE deficit periods. They developed a second index, based on buoyancy forces in the lower canopy, which outperformed u*. However, the index requires the measurement of turbulent statistics in the lower canopy, which we lack. Wohlfahrt et al. (2005) evaluated the effectiveness of low-u* data exclusion by comparing EC estimates of R with independent chamber measurements, scaled to the ecosystem. They found that low-u* data exclusion was necessary but not sufficient. The use of stationarity and integral turbulence tests to pre-screen the data (Foken and Wichura, 1996) reduced the impact of the low-u* exclusion and improved the agreement between EC and chamber estimates. Our findings are different. For the sites in this study, the use of stationarity and integral turbulence quality-control criteria (Foken and Wichura, 1996; Mahrt, 1998) did not significantly affect the estimates for CF or CFNEE or alter the relationships in Figs. 1 and 3. The most noteworthy difference between Figs. 1 and 3 occurred during unstable-daytime periods. Whereas CF increased linearly over the entire range of u*, CFNEE approached an asymptotic maximum of 1.03  0.04 at u* above 0.35 m s1. The contrasting responses of CF and CFNEE may be related to the interplay between CF, u*, and shear-driven versus convectively-driven turbulence. Because u* is a measure of shear-driven

A.G. Barr et al. / Agricultural and Forest Meteorology 140 (2006) 322–337

329

Fig. 2. Ten days mean energy-closure fraction CF plotted against the evaporative fraction EF. p is the probability of a type I error. The linear regression relationship is shown at SOA, where it was statistically significant at the 5% level.

turbulence, the linear dependence of CF on u* reflects with the increasing dominance of convectively-driven turbulence at low-u* (see Section 3.4 below). Unstabledaytime values of CFNEE are less sensitive than CF to u* because, unlike sensible heat and water vapor, CO2 is a passive scalar that does not contribute to convection. One caveat is necessary. Lee and Hu (2002) observed variations in CFNEE with surface conductance and cloudiness that they attributed to biological rather than physical factors. The implication is that CFNEE values below 1.0 do not necessarily demonstrate EC measurement limitations. While this is true, the similarities in

the behaviours of CFNEE and the physical parameter CF, combined with the similar CFNEE responses across three contrasting sites, provide support to the physical interpretation of the CFNEE–u* relationship. 3.3. The effect of atmospheric stability on energyclosure and FNEE Figs. 4 and 5 plot CF and CFNEE, respectively, as functions of the stability parameter j. Also shown are the eddy- and storage-flux components. The analysis is stratified by site and night versus day. Stability had a

Fig. 3. The effect of the friction velocity u* on the FNEE closure fraction CFNEE (upper panel) and its eddy-flux (middle panel) and storage-flux (lower panel) components, for nighttime, neutral-and-stable-daytime (j > 0.1), and unstable-daytime (j < 0.1) periods.

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Fig. 4. The effect of the atmospheric stability parameter j on the energy-closure fraction CF (upper panel) and its eddy-flux (middle panel) and storage-flux (lower panel) components, for nighttime and daytime periods. The j values represent very stable (j > 1), stable (0.01 < j < 1), neutral (0.01 < j < 0.01), unstable (1 < j < 0.01), and very unstable (j < 1) conditions.

Fig. 5. The effect of the atmospheric stability parameter j on the FNEE closure fraction CFNEE (upper panel) and its eddy-flux (middle panel) and storage-flux (lower panel) components, for nighttime and daytime periods. The j values represent very stable (j > 1), stable (0.01 < j < 1), neutral (0.01 < j < 0.01), unstable (1 < j < 0.01), and very unstable (j < 1) conditions.

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pronounced effect on both CF and CFNEE, with values ranging from 0.47  0.01 (CF) and 0.57  0.03 (CFNEE) under extreme stability at night to 0.91  0.03 (CF) and 1.10  0.04 (CFNEE) during mildly-unstable-daytime periods. The relationships were consistent across sites. At night, CF and CFNEE peaked at j of 0.1, corresponding to mildly-stable conditions, and declined at both stability extremes. The suppression of turbulence under strong stability caused the eddy-flux components to approach zero and the storage-flux components to increase, but the increase only partially offset the drop in the eddy-fluxes. CF and CFNEE also declined with increasing instability at night. However, the drop in CF may be an artifact of the analysis; j and CF are not independent because j is proportional to Hv. During the day, CF and CFNEE peaked at j of 0.04, corresponding to mixed convection, and declined at both stability extremes: sharply with increasing stability and subtly with increasing instability. To establish a more direct connection between CF and CFNEE (Fig. 6), we computed mean values of CF and CFNEE after stratifying the data by day and night and then binning the data by the 30 min energy-balance residual (H + lE  Rn  Q) into up to 10 bins of at least 500 points per bin. The binning produced a broad range of CF values, reflecting the high variability in the 30 min energy-balance residuals. More significantly, the analysis showed a strong positive relationship between CF and CFNEE for both day and night, strengthening the connection between CF and CFNEE. The slope was near one, with some tailing off of CFNEE at the highest values of CF.

Fig. 6. Variations in the FNEE closure fraction CFNEE as a function of the energy-closure fraction CF for nighttime periods with (Rn  Q) < 10 W m2 (closed symbols) and daytime periods with (Rn  Q) > 50 W m2 (open symbols).

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3.4. Causes of EC flux deficits The observed EC flux deficits may have instrumental or mechanistic causes (Barr et al., 1994; Mahrt, 1998; Massman and Lee, 2002). The EC systems used in this study, with their short tubes and fast flow rates, were designed to eliminate the need for spectral corrections and minimize other instrumental limitations. One plausible deficiency concerns the limited cosine response of the sonic anemometers (Gash and Dolman, 2003). The angle of attack operating range of 208 (van der Molen et al., 2004; Nakai et al., 2006) was sometimes exceeded, particularly at low wind speeds during the day. Based on empirical corrections, Nakai et al. (2006) reported upward adjustments to daytime H, lE, and F NEE at two forest sites of between 7 and 13%. The adjustments were similar for the CO2 and energy fluxes. The corresponding increase in CF (7–11%) closed the energy balance at one site but not the other. Three interrelated meteorological mechanisms may cause EC assumptions to be violated and produce flux deficits: non-zero horizontal and/or vertical advection, non-zero dispersive (mesoscale) fluxes, and lowfrequency atmospheric motions. During daytime convective conditions, Lenschow et al. (1994) showed that averaging periods that are short enough to meet the requirements for stationarity (typically 30–60 min) may be too short to fully resolve the low-frequency contributions to the flux. Malhi et al. (2004) reviewed the interaction between low-frequency atmospheric transport and EC flux measurements. They concluded that, while low-frequency motions can transfer a significant amount of the surface flux, the problem is not easily resolved. It is not sufficient to extend the sampling period beyond 30 or 60 min because other complications arise. The subtle decline in daytime CF and CFNEE with increasing instability (Figs. 4 and 5) may be related to an associated increase in lowfrequency contributions to the flux (Finnigan et al., 2003). The EC method may also underestimate fluxes in situations where transport by horizontal and vertical advection becomes significant (Finnigan, 1999; Lee, 1998; Mahrt, 1998; Paw et al., 2000; Massman and Lee, 2002). The occurrence of F NEE deficits on calm nights is well documented (Goulden et al., 1996; Black et al., 1996; Staebler and Fitzjarrald, 2004) and appears to be universal. It has been extensively studied because of its impact on annual NEP (Goulden et al., 1996; Barford et al., 2001; Barr et al., 2002; Morgenstern et al., 2004). The nighttime F NEE deficit is associated with extreme

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stability that suppresses turbulent mixing in the lower canopy; the lower canopy becomes decoupled from the above-canopy turbulence so that respired CO2 could be lost in horizontal, cold-air drainage flows (Staebler and Fitzjarrald, 2004; Aubinet et al., 2005; Goulden et al., 2006). Although we have not quantified horizontal advection at the BERMS sites, its potential importance has been demonstrated by Sun et al. (1998) who observed nocturnal venting of CO2 above a lake near the SOBS site, fed by horizontal inflow from the surrounding forest. The loss of respired CO2 by horizontal transport is not limited to sites with extreme topography (Staebler and Fitzjarrald, 2004). The relationship between mesoscale circulations and EC flux deficits is less well documented during the daytime. The observed dependencies of CF and CFNEE on u* and j during unstable-daytime periods (Figs. 1 and 3–5) were more subtle than those during nighttime and neutral-and-stable-daytime periods but equally well defined and consistent among sites. A large-eddy simulation study (Kanda et al., 2004) has linked the energy-closure problem to the development of mesoscale circulations on calm days. Turbulent organized structures in the convective boundary layer that resulted from inhomogeneous surface heating caused the nearsurface ‘‘turbulent’’ flux to systematically underestimate the ‘‘total’’ flux because of spatially organized scalar transport from horizontal and vertical advection.

Kanda et al. simulated an increase in CF from 80 to 92% to 97% as the geostrophic wind speed increased from 0 to 1 to 4 m s1, respectively. This increase is consistent with the unstable-daytime CF response in Fig. 1 (right panel). 3.5. Implications for the post-processing of EC data Figs. 1–6 have important implications for the interpretation and processing of EC flux data. The CF–u* and CFNEE–u* relationships (Figs. 1 and 3) substantiate the common practice of excluding F NEE data at low values of u* during nighttime and neutraland-stable-daytime periods, when a distinct u* threshold can be identified. They also demonstrate the need to extend the exclusions to H and lE. However, the exclusion of low-u* data does not eliminate the problem of energy balance non-closure. At night, CF became stable at u* above 0.35 m s1 but a small energy imbalance remained. The high-u* nighttime CF mean (0.91) was similar to the daytime mean (0.88). During unstable-daytime periods, CF was linearly related to u*, with no identifiable u* threshold. The consistent and well-defined relationship between CF and u* (Fig. 1) suggests the possibility of a u*dependent energy-closure adjustment scheme. The similar responses of CF and CFNEE to time of day, u* and j (Figs. 1 versus 3 and 4 versus 5) and the

1 2 Fig. 7. The effect of the u* exclusion threshold uth year1, lower panels).  on mean annual E (mm year , upper panel) and NEP, R and P (g C m The two lines for E represent two low-u* exclusion periods (N, nighttime only; N&D, both night and day). The three lines for NEP, R and P combine these exclusion periods with two equations to estimate daytime R (see legend). Note the different scales for NEP, R and P.

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direct correspondence between CF and CFNEE (Fig. 6) show that the energy and CO2 flux deficits are interrelated. The association lends support to the application of energy-closure adjustments to F NEE as well as H and lE but it is not conclusive. If applied, energy-closure adjustments for F NEE should be derived from the CF analysis, which provides an absolute measure of the EC flux deficits. In the following two analyses, we explore the sensitivity of annual E, F NEE, R, and P to low-u* data exclusion and energy-closure adjustments, and make recommendations for the routine post-processing of EC data. 3.5.1. Low-u* data exclusion Fig. 7 plots mean annual E, NEP, R, and P as a function of the u* exclusion threshold uth  for three gapfilling approaches, with no energy-closure adjustments. In general, E, R, and P increased with increasing uth , with R and P but not E reaching stable maxima at uth  above 0.40 m s1. The continuing rise in E above uth  of 0.40 m s1 was related to the daytime energy imbalance at high-u* (Fig. 1). The response of NEP to uth  was less consistent among sites and more difficult to interpret. The three gap-filling approaches produced similar 1 th NEP–uth but  relationships at u below 0.20 m s 1 th diverged at u above 0.20 m s . The simplest approach (Eq. (13) with nighttime low-u* data exclusion) gave the most stable and consistent results, with NEP, R, and P 1 stabilizing at uth  above 0.30–0.40 m s . The other approaches (with low-u* data excluded both night and

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of day) produced less stable results above uth  0.40 m s1, particularly at the coniferous sites. Fig. 8 shows the sensitivity of mean annual NEP, R, and P to the low-u* exclusion period for a uth  of 0.35 m s1, with gaps filled by four variations on a standard method (Section 2.5). The four variations were alike in the use of Eqs. (13) and (15) to fill gaps in nighttime R and daytime P, respectively, but differed in the use of Eq. (13) versus Eq. (14) to estimate daytime R and in the use of Ts versus Ta as the independent variable in Eq. (13). Annual NEP (upper panel) was similar for all gap-filling approaches but responded sensitively to nighttime low-u* data exclusion, with little additional response to daytime exclusion. Individual years (not shown) responded similarly to the 5 year mean. Fig. 8 (middle and lower panels) shows the associated responses of annual R and P to uth  . Not only did gap-filled R and P respond more sensitively than NEP to the low-u* exclusion period but their responses varied among gap-filling approaches. The two approaches that estimated daytime R using the nighttime R = f(T) relationship (Eq. (13)) were sensitive to nighttime low-u* exclusion but showed little additional response to daytime exclusion. R and P but not NEP were affected by the use of Ts versus Ta in Eq. (13). In contrast, the two approaches that estimated daytime R using the daytime light-response curve (Eq. (14)) responded to both nighttime and daytime low-u* exclusion. The annual R estimates from (13) and (14) diverged when low-u* data were excluded during

Fig. 8. Mean annual NEP, R and P (g C m2 y1) as a function of the low-u* exclusion period (n/a, none; N, nighttime only; N&S, nighttime and neutral-and-stable-daytime (j > 0.1); N&D, both night and day). The four curves differ in the equations used to estimate daytime R and to fill gaps in R at night (see legend).

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the nighttime only but converged when low-u* data were excluded both day and night. The convergence was caused by the sensitivity of the light-response method (14) to the low-u* F NEE data at dawn and dusk. Although a sensitivity analysis cannot be used to identify which gap-filling approach is best, the convergence of gap-filling approaches for combined nighttime and daytime exclusion adds some support to the associated values of R and P. At each site, the associated values from Eq. (14) were bounded by the f(Ts) and f(Ta) values from Eq. (13). 3.5.2. Energy-closure adjustments Energy-closure adjustment schemes often multiply both H and lE by CF1, thus preserving the ratio of H/lE (Blanken et al., 1998; Barr et al., 2000; Twine et al., 2000). Here we evaluate the impact of several approaches to estimate CF on annual E, NEP, R, and P. The CF estimates included site-specific or cross-site means from Table 2, in combination with three stratifications (24 h means; separate nighttime, neutral-and-stable-daytime, and unstable-daytime means; and u*-dependent CF values from Fig. 1). Of the four fluxes, E had the simplest and most consistent response to the energy-closure adjustments. The adjustments increased E by 9–21% (15  3%, mean  s.d.), with the greatest increase (>19%) for the u*-dependent schemes. The three sites responded similarly. Because nighttime E was near zero, the adjusted E values were insensitive to day–night differences in CF. Imbalanced partitioning of the energy imbalance between H and lE, such as was observed at SOA (Fig. 2), would produce an even greater increase in E. Mean annual NEP had a much broader range of responses to the energy-closure adjustment schemes. The schemes that used a single adjustment factor simply scaled NEP, R, and P by CF1. However, the schemes that separated daytime and nighttime adjustments produced a disproportionately large increase in NEP (35  18%, mean  s.d.); the day–night differences in CF (Table 2) caused P to increase more than R. The simpler schemes produced consistent responses among sites whereas the more complex schemes diverged. Annual NEP, R, and P were particularly sensitive to the inter-site differences in nighttime CF (Table 2), even though these differences were within the measurement uncertainty in CF and may not be real. We conclude that, when energy-closure adjustments are made to F NEE, they must be applied with care. We favour the simpler adjustment schemes, such as a single adjustment factor, because they produce the most consistent results.

The need for energy-closure adjustments to E is supported by two independent studies of the water balance at the BERMS sites. Barr et al. (2000) used a novel measurement technique based on the deep groundwater pore pressure to study the water balance at SOA during 1997 and 1998. The water balance closed only when a 15% energy-closure adjustment was applied to E. An unpublished, multi-year analysis of the vertical water budget (precipitation minus evapotranspiration minus storage change) in relation to gauged streamflow at the BERMS sites also corroborates the need for an 15% increase in E to close the water balance and produce reasonable (residual) estimates of runoff (Garth van der Kamp, Environment Canada, personal communication). The application of an energy-closure adjustment to F NEE, although less certain, is also supported by independent measurements. At SOA, Gaumont-Guay et al. (2006) found that chamber measurements of R, scaled to the stand, exceeded EC estimates of R by 40%. The gap was narrowed to 24% when an energy-closure adjustment was applied to F NEE. Work is in progress to further substantiate the nighttime CFNEE–u* analysis in Fig. 3 using upscaled chamber measurements of R to replace modelled Fˆ NEE in the calculation of CFNEE. 4. Summary and conclusions 1. This study evaluated energy-balance closure by the eddy-covariance method above three contrasting tall forests and attempted to establish a link between the energy and CO2 flux deficits. 2. The energy-closure fraction CF was consistent among sites, varying from 85 to 89%. At night, CF became stable (91  9%) at u* values above 0.35 m s1 and declined at lower values of u*. During the day, CF was linearly dependent on u*, increasing from 70  3% at low-u* to 96  2% at high-u*. 3. The similarities in the energy imbalance patterns above the three sites were striking. The sites were separated by tens of kilometres in a similar landscape setting. The pattern appears to be characteristic of continental boreal forests. 4. Similarities were also observed between the CO2 and energy imbalances. CF and the F NEE closure fraction CFNEE responded similarly to u*, j and time of day. 5. The analysis substantiated the common practice of rejecting EC F NEE measurements during low-u* periods at night and extended the exclusion to H and lE. It also showed that F NEE should be rejected during daytime, low-u* periods when daytime R was estimated from an empirical light-response curve.

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6. The analysis added support to the application of energy-closure adjustments to F NEE as well as H and lE but was not conclusive. Independent estimates of E and R were cited that upheld the need for energyclosure adjustments at these sites. Acknowledgements We gratefully acknowledge the contributions of Zoran Nesic, Andrew Sauter, Rick Ketler, Shawn O’Neill, Don Zuiker, and Dan Finch, who provided laboratory, field, and data management support for the flux measurements; Natascha Kljun, Praveena Krishnan, and Sheila McQueen, who quality assured the EC data; Joe Eley, Charmaine Hrynkiw, Dell Bayne, Natasha Neumann, Erin Thompson, and Steve Enns, who oversaw the meteorological measurements and data management; and Barry Goodison, Bob Stewart, and Norm Stolle, who served on the BERMS Management Committee and championed the BERMS program. We also thank Xuhui Lee and one anonymous reviewer for their helpful and constructive comments. Financial support was provided by the Climate Research Branch of the Meteorological Service of Canada, the Canadian Forest Service, Parks Canada, the Action Plan 2000 on Climate Change, the Program of Energy Research and Development, the Climate Change Action Fund, the Natural Sciences and Engineering Research Council of Canada, the Canadian Foundation for Climate and Atmospheric Science, and the BIOCAP Canada Foundation. References Amiro, B.D., Barr, A.G., Black, T.A., Iwashita, H., Kljun, N., McCaughey, J.H., Morgenstern, K., Murayama, S., Nesic, Z., Orchansky, A.L., Saigusa, N., 2006. Carbon, energy and water fluxes at mature and disturbed forest sites, Saskatchewan, Canada. Agric. Forest Meteorol. 136, 237–251. Amthor, J.S., Chen, J.M., Clein, J.S., Frolking, S.E., Goulden, M.L., Grant, R.F., Kimball, J.S., King, A.W., McGuire, A.D., Nikolov, N.T., Potter, C.S., Wng, S., Wofsy, S.C., 2001. Boreal forest CO2 exchange and evapotranspiration predicted by nine ecosystem process models: intermodel comparisons and relationships to field measurements. J. Geophys. Res. 106, 33623–33648. Arain, M.A., Black, T.A., Barr, A.G., Jarvis, P.G., Massheder, J.M., Verseghy, D.L., Nesic, Z., 2002. Effects of seasonal and interannual climate variability on net ecosystem productivity of boreal deciduous and conifer forests. Can. J. Forest Res. 32, 878–891. ¨ ., Moncrieff, J., Foken, T., Aubinet, M., Grelle, A., Ibrom, A., Rannik, U Kowalski, A.S., Martin, P.H., Berbigier, P., Bernhofer, Ch., Clement, R., Elbers, J., Granier, A., Gru¨nwald, T., Morgenstern, K., Pilegaard, K., Rebmann, C., Snijders, W., Valentini, R., Vesala, T., 2000. Estimates of the net annual carbon and water exchange of forests: the EUROFLUX methodology. Adv. Ecol. Res. 30, 113–175.

335

Aubinet, M., Berbigier, P., Bernhofer, C., Cescatti, A., Feigenwinter, C., Granier, A., Grunwald, T., Havrankova, K., Heinesch, B., Longdoz, B., Marcolla, B., Montagnani, L., Sedlak, P., 2005. Comparing CO2 storage and advection conditions at night at different CARBOEUROFLUX sites. Boundary-Layer Meteorol. 116, 63–94. Baldocchi, D.D., Vogel, A.V., Hall, B., 1997. Seasonal variation of energy and water vapor exchange rates above and below a boreal jack pine forest canopy. J. Geophys. Res. 102 (D24), 28939–28951. Baldocchi, D., Falge, E., Gu, L., Olson, R., Hollinger, D., Running, S., Anthoni, P., Bernhofer, Ch., Davis, K., Evans, R., Fuentes, J., Goldstein, A., Katul, G., Law, B.E., Lee, X., Malhi, Y., Meyers, T., Munger, W., Oechel, W., Paw, U.K.T., Pilegaard, K., Schmid, H.P., Valentini, R., Verma, S., Vesala, T., Wilson, K., Wofsy, S., 2001. FLUXNET: a new tool to study the temporal and spatial variability of ecosystem-scale carbon dioxide, water vapor, and energy flux densities. Bull. Am. Meteorol. Soc. 82, 2415–2434. Barford, C.C., Wofsy, S.C., Goulden, M.L., Munger, J.W., Pyle, E.H., Urbanski, S.P., Hutyra, L., Saleska, S.R., Fitzjarrald, D., Moore, K., 2001. Factors controlling long- and short-term sequestration of atmospheric CO2 in a mid-latitude forest. Science 294, 1688–1691. Barr, A.G., King, K.M., Gillespie, T.J., den Hartog, G., Neumann, H.H., 1994. A comparison of Bowen ratio and eddy correlation sensible and latent heat flux measurements above deciduous forest. Boundary-Layer Meteorol. 71, 21–41. Barr, A.G., Betts, A.K., 1997. Radiosonde boundary-layer budgets above a boreal forest. J. Geophys. Res. 102 (D24), 29205–29212. Barr, A.G., van der Kamp, G., Schmidt, R., Black, T.A., 2000. Monitoring the moisture balance of a boreal aspen forest using a deep groundwater piezometer. Agric. Forest Meteorol. 102, 13–24. Barr, A.G., Betts, A.K., Black, T.A., McCaughey, J.H., Smith, C.D., 2001. Intercomparison of BOREAS northern and southern study area surface fluxes in 1994. J. Geophys. Res. 106, 33543–33550. Barr, A.G., Griffis, T.G., Black, T.A., Lee, X., Staebler, R.M., Fuentes, J.D., Chen, Z., Morgenstern, K., 2002. Comparing the carbon budgets of boreal and temperate deciduous forest stands. Can. J. Forest Res. 32, 813–822. Barr, A.G., Black, T.A., Hogg, E.H., Kljun, N., Morgenstern, K., Nesic, Z., 2004. Inter-annual variability in the leaf area index of a boreal aspen-hazelnut forest in relation to net ecosystem production. Agric. Forest Meteorol. 126, 237–255. Barr, A.G., Black, T.A., Hogg, E.H., Griffis, T., Morgenstern, K., Kljun, N., Theede, A., Nesic, Z. Climatic controls on the carbon and water budgets of a boreal aspen forest. Global Change Biol., doi:10.1111/j.1365-2486.2006.01220.x, in press. Betts, A.K., Ball, J.H., Barr, A.G., Black, T.A., McCaughey, J.H., Viterbo, P. Analysis of ERA-40 biases and surface-BL-cloud coupling using BERMS data. Agric. Forest Meteorol., submitted for publication. Black, T.A., den Hartog, G., Neumann, H.H., Blanken, P.D., Yang, P.C., Russel, C., Nesic, Z., Lee, X., Chen, S.G., Staebler, R., Novak, M.D., 1996. Annual cycles of water vapor and carbon dioxide fluxes in and above a boreal aspen forest. Global Change Biol. 2, 219–229. Black, T.A., Chen, W.J., Barr, A.G., Arain, M.A., Chen, Z., Nesic, Z., Hogg, E.H., Neumann, H.H., Yang, P.C., 2000. Increased carbon sequestration by a boreal deciduous forest in years with a warm spring. Geophys. Res. Lett. 27, 1271–1274. Blanken, P.D., Black, T.A., Yang, P.C., Neumann, H.H., Nesic, Z., Staebler, R., 1997. Energy balance and canopy conductance of a boreal aspen forest: partitioning overstory and understory components. J. Geophys. Res. 102 (D24), 28915–28927.

336

A.G. Barr et al. / Agricultural and Forest Meteorology 140 (2006) 322–337

Blanken, P.D., Black, T.A., Neumann, H.H., den Hartog, G., Yang, P.C., Nesic, Z., Staebler, R., Chen, W., Lee, X., 1998. Turbulent flux measurements above and below the overstory of a boreal aspen forest. Boundary-Layer Meteorol. 89, 109–140. Chen, W.J., Black, T.A., Yang, P.C., Barr, A.B., Neumann, H.H., Nesic, Z., Blanken, P.D., Novak, M.D., Eley, J., Ketler, R.J., Cuenca, R., 1999. Effects of climatic variability on the annual carbon sequestration by a boreal aspen forest. Global Change Biol. 5, 41–53. Coursolle, C., Margolis, H.A., Barr, A.G., Black, T.A., Amiro, B.D., McCaughey, J.H., Flanagan, L.B., Lafleur, P.M., Roulet, N.T., Bourque, C.P.-A., Arain, M.A., Wofsy, S.C., Dunn, A., Morgenstern, K., Orchansky, A.L., Bernier, P.Y., Chen, J.M., Kidston, J., Saigusa, N., Hedstrom, N., 2006. Late-summer carbon fluxes from Canadian forests and peatlands along an east-west continental transect. Can. J. Forest Res. 36, 783–800. Desjardins, R.L., MacPherson, J.I., Mahrt, L., Schuepp, P., Pattey, E., Neumann, H., Baldocchi, D., Wofsy, S., Fitzjarrald, D., McCaughey, H., Joiner, D.W., 1997. Scaling up flux measurements from the boreal forest using aircraft-tower combinations. J. Geophys. Res. 102 (D24), 29125–29133. Fairall, C.W., Persson, P.O.G., Bradley, E.F., Payne, R.E.R., Anderson, S.P., 1998. A new look at calibration and use of Eppley precision infrared radiometers Part I: Theory and application. J. Atmos. Oceanic Technol. 15, 1229–1242. Falge, E., Baldocchi, D., Tenhunen, J., Aubinet, M., Bakwin, P., Berbigier, P., Bernhofer, C., Burba, G., Clement, R., Davis, K.J., Elbers, J.A., Goldstein, A.H., Grelle, A., Granier, A., Guomundsson, J., Hollinger, D., Kowalski, A.S., Katul, G., Law, B.E., Malhi, Y., Meyers, T., Monson, R.K., Munger, J.W., Oechel, W., Paw, U.K.T., Pilegaard, K., Rannik, U., Rebmann, C., Suyker, A., Valentini, R., Wilson, K., Wofsy, S., 2002. Seasonality of ecosystem respiration and gross primary production as derived from FLUXNET measurements. Agric. Forest Meteorol. 113, 53–74. Finnigan, J., 1999. A comment on the paper by Lee (1998): on micrometeorological observations of surface-air exchange over tall vegetation. Agric. Forest Meteorol. 97, 55–64. Finnigan, J.J., Clement, R., Malhi, Y., Leuning, R., Cleugh, H.A., 2003. A re-evaluation of long-term flux measurement techniques—Part I: Averaging and coordinate rotation. Boundary-Layer Meteorol. 107, 1–48. Foken, T., Wichura, B., 1996. Tools for quality assessment of surfacebased flux measurements. Agric. Forest Meteorol. 78, 83–105. Gash, J.H.C., Dolman, A.J., 2003. Sonic anemometer (co)sine response and flux measurement I. The potential for (co) sine error to affect sonic anemometer-based flux measurements. Agric. Forest Meteorol. 119, 195–207. Garratt, J.R., 1992. The Atmospheric Boundary Layer. Cambridge University Press, Cambridge, 316 pp. Gaumont-Guay, D., Black, T.A., Griffis, T.J., Barr, A.G., Morgenstern, K., Jassal, R.S., Nesic, Z., 2006. Influence of temperature and drought on seasonal and interannual variations of soil, bole and ecosystem respiration in a boreal aspen stand. Agric. Forest Meteorol. 140, 203–219. Goulden, M.L., Munger, J.W., Fan, S.-M., Daube, B.C., Wofsy, S.C., 1996. Measurements of carbon sequestration by long-term eddy covariance: methods and critical evaluation of accuracy. Gobal Change Biol. 2, 169–182. Goulden, M.L., Wofsy, S.C., Harden, J.W., Trumbore, S.E., Crill, P.M., Gower, S.T., Fries, T., Daube, B.C., Fan, S.-M., Sutton, D.J., Bazzaz, A., Munger, J.W., 1998. Sensitivity of boreal forest carbon balance to soil thaw. Science 279, 214–217.

Goulden, M.L., Miller, S.D., da Rocha, H.R., 2006. Nocturnal cold air drainage and pooling in a tropical forest. J. Geophys. Res. 111 (D8), D08S04, doi:10.1029/2005JD006037. Gower, S.T., Vogel, J.G., Norman, J.M., Kucharick, C.J., Steele, S.J., Sow, T.K., 1997. Carbon distribution and above ground net primary production in aspen, jack pine, and black spruce stands in Saskatchewan and Manitoba, Canada. J. Geophys. Res. 102 (D24), 29029–29041. Grant, R.F., Black, T.A., den Hartog, G., Berry, J.A., Neumann, H.H., Blanken, P.D., Yang, P.C., Russell, C., Nalder, I.A., 1999. Daily and annual exchanges of mass and energy between an aspenhazelnut forest and the atmosphere: testing the mathematical model Ecosys with data for the BOREAS experiment. J. Geophys. Res. 104, 27699–27717. Grant, R.F., Jarvis, P.G., Massheder, J.M., Hale, S.E., Moncrieff, J.B., Rayment, M., Scott, S.L., Berry, J.A., 2001. Controls on carbon and energy exchange by a black spruce–moss ecosystem: testing the mathematical model Ecosys with data from the BOREAS experiment. Global Biogeochem. Cycles 15, 129–147. Griffis, T.J., Rouse, W.R., Waddington, J.M., 2000. Interannual variability in net ecosystem CO2 exchange at a subarctic fen. Global Biogeochem. Cycles 14, 1109–1121. Griffis, T.J., Black, T.A., Morgenstern, K., Barr, A.G., Nesic, Z., Drewitt, G.B., Gaumont-Guay, D., McCaughey, J.H., 2003. Ecophysiological controls on the carbon balances of three southern boreal forests. Agric. Forest Meteorol. 117, 53–71. Jarvis, P.G., Massheder, J.M., Hale, S.E., Moncrieff, J.B., Rayment, M., Scott, S.L., 1997. Seasonal variation of carbon dioxide, water vapor, and energy exchanges of a boreal black spruce forest. J. Geophys. Res. 102 (D24), 28953–28966. Kanda, M., Ianagaki, A., Letzel, M.O., Raasch, S., Watanabe, T., 2004. LES study of the energy imbalance problem with eddy covariance fluxes. Boundary-Layer Meteorol. 110, 381–404. Kljun, N., Black, T.A., Griffis, T., Barr, A., Gaumont-Guay, D., McCaughey, J.H., Morgenstern, K., Nesic, Z. Response of net ecosystem productivity of three boreal forest stands to drought. Ecosystems, in press. Lee, X.H., 1998. On micrometeorological observations of surfaceair exchange over tall vegetation. Agric. Forest Meteorol. 91, 39–49. Lee, X., Hu, X., 2002. Forest-air fluxes of carbon, water and energy over non-flat terrain. Boundary-Layer Meteorol. 103, 277–301. Lenschow, D.H., Mann, J., Kristensen, L., 1994. How long is long enough when measuring fluxes and other turbulence statistics? J. Atmos. Oceanic Technol. 11, 661–673. Li, Z.Q., Yu, G.R., Wen, X.F., Zhang, L.M., Re, C.Y., Fu, Y.L., 2005. Energy balance closure at ChinaFLUX sites. Sci. China Ser. DEarth Sci. 48, 51–62. Mahrt, L., 1998. Flux sampling errors for aircraft and towers. J. Atmos. Oceanic Technol. 15, 416–429. Malhi, Y., McNaughton, K., Von Radow, C., 2004. Low frequency atmospheric transport and surface flux measurements. In: Lee, X., Massman, W., Law, B. (Eds.), Handbook of Micrometeorology: A Guide for Surface Flux Measurement and Analysis. Kluwer Academic Publishers, Dordrecht, pp. 101–118. Massman, W.J., Lee, X., 2002. Eddy covariance flux corrections and uncertainties in long-term studies of carbon and energy exchanges. Agric. Forest Meteorol. 113, 121–144. McCaughey, J.H., Saxton, W.L., 1988. Energy balance storage terms in a mixed forest. Agric. Forest Meteorol. 44, 1–18. Morgenstern, K., Black, T.A., Humphreys, E.R., Griffis, T.J., Drewitt, G.B., Cai, T., Nesic, Z., Spittlehouse, D.L., Livingston, N.J., 2004.

A.G. Barr et al. / Agricultural and Forest Meteorology 140 (2006) 322–337 Sensitivity and uncertainty of the carbon balance of a pacific Northwest Douglas-fir forest during an El Nino/La Nina cycle. Agric. Forest Meteorol. 123, 201–219. Nakai, T., van der Molen, M.K., Gash, J.H.C., Kodama, Y., 2006. Correction of sonic anemometer angle of attack errors. Agric. Forest Meteorol. 136, 19–30. Paw, U.K.T., Baldocchi, D.D., Meyers, T.P., Wilson, K.B., 2000. Correction of eddy-covariance measurements incorporating both advective effects and density fluxes. Boundary-Layer Meteorol. 97, 487–511. Sellers, P.J., Hall, F., Margolis, H., Kelly, B., Baldocchi, D., den Hartog, G., Cihlar, J., Ryan, M.G., Goodison, B., Crill, P., Ranson, K.J., Lettenmaier, D., Wickland, D.E., 1995. The Boreal Ecosystem-Atmosphere Study (BOREAS): an overview and early results from the 1994 field year. Bull. Am. Meteorol. Soc. 77, 1549–1577. Sellers, P.J., Hall, F.G., Kelly, R.D., Black, A., Baldocchi, D., Berry, J., Margolis, H., Ryan, M., Ranson, J., Crill, P., Lettenmaier, D., Cihlar, J., Newcomer, J., Halliwell, D., Fitzjarrald, D., Jarvis, P., Gower, S., Williams, D., Goodison, B., Wickland, D., Guertin, F., 1997. BOREAS in 1997: Experiment overview, scientific results, and future directions. J. Geophys. Res. 102 (D24), 28731–28770. Staebler, R.M., Fitzjarrald, D.R., 2004. Observing subcanopy CO2 advection. Agric. Forest Meteorol. 122, 139–156.

337

Sun, J.L., Desjardins, R., Mahrt, L., MacPherson, I., 1998. Transport of carbon dioxide, water vapor, and ozone by turbulence and local circulations. J. Geophys. Res. 103, 25873–25885. Twine, T.E., Kustas, W.P., Norman, J.M., Cook, D.R., Houser, P.R., Meyers, T.P., Prueger, J.H., Starks, P.J., Wesely, M.L., 2000. Correcting eddy-covariance flux underestimates over a grassland. Agric. Forest Meteorol. 103, 279–3000. van der Molen, M.K., Gash, J.H.C., Elbers, J.A., 2004. Sonic anemometer cosine response and flux measurement. II. The effect of introducing an angle of attack dependence calibration. Agric. Forest Meteorol. 122, 95–109. Wilson, K., Goldstein, A., Falge, E., Aubinet, M., Baldocchi, D., Berbigier, P., Bernhofer, C., Ceulemans, R., Dolman, H., Field, C., Grelle, A., Ibrom, A., Law, B.E., Kowalski, A., Meyers, T., Moncrieff, J., Monson, R., Oechel, W., Tenhunen, J., Valentini, R., Verma, S., 2002. Energy balance closure at FLUXNET sites. Agric. Forest Meteorol. 113, 223–243. Wohlfahrt, G., Anfang, C., Bahn, M., Haslwanter, A., Newesely, C., Schmitt, M., Drosler, M., Pfadenhauer, J., Cernusca, A., 2005. Quantifying nighttime respiration of a meadow using eddy covariance, chambers and modelling. Agric. Forest Meteorol. 128, 141– 162. Yang, P.C., Black, T.A., Neumann, H.H., Novak, M.D., Blanken, P.D., 1999. Spatial and temporal variability of CO2 concentration and flux in a boreal aspen forest. J. Geophys. Res. 104, 27653–27661.